Senior Data Tester

Responsibilities

Design and execute ETL test scenarios, test cases and test scripts based on business and technical requirements.

Validate source-to-target data mapping, transformation rules, business rules and data flows.

Perform ETL, Data Warehouse and Database Testing across multiple data sources and target systems.

Validate data extraction, transformation, loading and reconciliation processes.

Perform data validation, data completeness, data accuracy and data integrity testing.

Write complex SQL queries for backend data validation, reconciliation and defect analysis.

Validate source-to-target mappings and identify data discrepancies. Perform database testing involving joins, stored procedures, views, functions, indexes and constraints.

Test incremental loads, full loads, CDC and batch processing where applicable.

Validate ETL workflows, schedules, dependencies and error-handling mechanisms.

Perform data reconciliation between source and target systems and investigate mismatches.

Validate duplicate records, missing records, null values, data truncation and transformation errors.

Execute regression, integration, system and end-to-end testing for ETL/data pipelines.

Validate large-volume datasets and perform data quality and consistency checks.

Work with developers, data engineers, business analysts and product teams to resolve data-related issues.

Analyze ETL job failures and assist development teams with root-cause analysis (RCA). Log, track and manage defects using tools such as Jira, Azure DevOps or similar.

Prepare test execution reports, defect reports and testing status updates.

Participate in requirement analysis, test planning, estimation and defect triage meetings.

Support UAT, production validation and post-release data verification.

Ensure testing complies with enterprise data governance, security, privacy and quality standards.

Work in an Agile/Scrum environment and participate in sprint planning, daily stand-ups, reviews and retrospectives.

Collaborate with globally distributed teams and stakeholders across business and technology functions.

Understand insurance-domain data such as policy, customer, claims, billing, premium and financial data is an advantage.

Requirements

4–6 years of hands-on experience in ETL Testing / Data Warehouse Testing / Database Testing.

Strong understanding of ETL concepts, data warehousing and data integration processes.

Strong hands-on SQL skills including complex joins, subqueries, CTEs, aggregations and data reconciliation queries.

Experience with ETL tools such as Informatica, IBM DataStage, SSIS, Talend, Azure Data Factory or similar.

Experience testing large-volume data and enterprise data pipelines. Good understanding of Dimensional Data Modeling, Star Schema, Snowflake Schema, Fact and Dimension tables.

Strong knowledge of source-to-target mapping and transformation validation.

Experience with Oracle, SQL Server, DB2, PostgreSQL or other relational databases.

Knowledge of batch processing, scheduling, incremental/full loads and data migration testing.

Experience with API, web service or downstream application data validation is preferred.

Good understanding of SDLC, STLC, defect lifecycle and Agile methodologies.

Experience with Jira, Azure DevOps, ALM or similar test/defect management tools.

Exposure to cloud data platforms such as Azure/AWS is an added advantage.

Exposure to Python or scripting for test-data validation/automation is desirable.

Knowledge of data quality, reconciliation, data lineage and data governance.

Strong analytical and problem-solving skills with the ability to investigate complex data issues.

Excellent communication and stakeholder-management skills.

Prior experience in Insurance, BFSI or other regulated enterprise environments will be highly preferred.

Job Summary

We are looking for an experienced ETL Tester / Data QA Engineer with strong SQL skills to validate data pipelines, ETL processes, data transformations, and data quality across source and target systems. The candidate should have hands-on experience in writing complex SQL queries, performing data validation, and identifying data discrepancies.

Key Responsibilities

• Perform ETL testing for data extraction, transformation, and loading processes.
• Validate data between source and target systems.
• Write and execute complex SQL queries for data validation and reconciliation.
• Verify data transformations, mappings, joins, aggregations, and business rules.
• Perform data completeness, accuracy, consistency, integrity, and duplicate checks.
• Validate incremental and full data loads.
• Test ETL workflows, batch jobs, and data pipelines.
• Identify, document, and track defects using tools such as JIRA.
• Analyze production data issues and support root-cause analysis.
• Prepare test scenarios, test cases, test data, and execution reports.
• Work closely with developers, data engineers, business analysts, and other QA teams.
• Participate in regression, integration, system, and end-to-end testing.

Required Skills

• Strong hands-on experience in ETL/Data Warehouse Testing.
• Advanced SQL skills, including:
• Joins
• Subqueries
• CTEs
• Window functions
• Aggregations
• Stored procedures
• Data reconciliation
• Knowledge of Data Warehousing concepts such as fact and dimension tables, star/snowflake schemas, and slowly changing dimensions (SCD).
• Experience with ETL tools such as Informatica, Talend, SSIS, DataStage, or similar.
• Experience with relational databases such as Oracle, SQL Server, PostgreSQL, or MySQL.
• Good understanding of SDLC/STLC and defect life cycle.
• Experience with Agile/Scrum methodologies.

Good to Have

• Experience with cloud data platforms such as Snowflake, AWS, Azure, or GCP.
• Knowledge of tools such as Databricks, Azure Data Factory, AWS Glue, or dbt.
• Experience with API testing or data pipeline automation.
• Basic knowledge of Python for test/data validation automation.
• Experience testing large-volume datasets and complex data migrations.

Qualifications

• Bachelor's degree in Computer Science, IT, Engineering, or a related field.
• Strong analytical and problem-solving skills.
• Good communication and documentation skills.

Role Overview

We are seeking a skilled Senior Data Analyst with substantial hands-on experience in Source System Analysis, data mapping, data modeling, SQL analysis, and integration testing. The ideal candidate will support seamless data flow across business applications, ensure data accuracy, and contribute to system improvements.

Key Responsibilities

• Conduct detailed data mapping between source and target systems, ensuring consistency and accuracy.
• Build and maintain data models to support business processes, reporting needs, and integration workflows.
• Write and optimize SQL queries for data validation, analysis, migration, and troubleshooting.
• Work closely with cross-functional teams to gather data requirements and understand business logic.
• Develop and execute integration test plans, including functional, data validation, and regression testing.
• Support data migration activities, including extraction, transformation, and loading (ETL).
• Monitor and troubleshoot integration issues, ensuring timely resolution.
• Document integration flows, data dictionaries, mapping catalogs, and knowledge artifacts.
• Ensure integration security, data integrity, and compliance with organizational standards.

Required Skills & Qualifications

• 5+ years of experience insource system integrations, data mapping, and system-to-system data workflows.
• Bachelor’s degree in Computer Science, Information Systems, Engineering, or related field.
• Hands-on experience with data mapping, data modelling, and database schema design.
• Proficiency in SQL (complex joins, stored procedures, performance tuning).
• Experience with ETL tools or integration platforms (Informatica, Boomi, MuleSoft, Talend, Pentaho).
• Strong analytical and problem-solving skills.
• Experience with API-based integrations (REST/SOAP) is a plus.
• Familiarity with testing methodologies for integrations and data validation.
• Ability to work with business users, understand workflows, and translate requirements into technical solutions.

Preferred Qualifications

• Experience with cloud ERP or cloud integration (AWS/Azure/GCP).
• Knowledge of JSON, XML, CSV transformation.
• Exposure to scripting languages (Python, Shell).
• Experience in Agile/Scrum environments.
• Prior involvement in ERP implementation or upgrade projects.

Competencies

• Tech Savvy - Anticipating and adopting innovations in business-building digital and technology applications.
• Self-Development - Actively seeking new ways to grow and be challenged using both formal and informal development channels.
• Action Oriented - Taking on new opportunities and tough challenges with a sense of urgency, high energy, and enthusiasm.
• Customer Focus - Building strong customer relationships and delivering customer-centric solutions.
• Optimize Work Processes - Knowing the most effective and efficient processes to get things done, with a focus on continuous improvement.

Why Join Us?

• Be part of a collaborative and agile team driving cutting-edge AI and data engineering solutions.
• Work on impactful projects that make a difference across industries.
• Opportunities for professional growth and continuous learning.
• Competitive salary and benefits package.

Application Details

Ready to make an impact? Apply today and become part of the QX Impact team!

Job Description – QA & Data Validation Engineer

Experience: 5–6 Years

Location: Pan India

Employment Type: Full-Time

Work Mode: Pan India / Remote or Hybrid as applicable

About the Role

We are looking for an experienced QA & Data Validation Engineer with 5–6 years of hands-on experience in data quality assurance, solution analysis, data validation, SQL, Python, PySpark, Azure Data Factory, Azure Synapse Analytics, and Power BI validation.

The ideal candidate will be responsible for validating large-scale data pipelines, performing source-to-target reconciliation, analyzing business rules, investigating data defects, and ensuring the accuracy, completeness, and consistency of data across source, staging, intermediate, and target systems.

The role requires strong analytical and problem-solving skills along with the ability to work closely with development, data engineering, business, and other stakeholders in an Agile delivery environment.

You will also contribute to the design, development, and maintenance of automated validation frameworks and utilities using Python, SQL, PySpark, Azure Data Factory, and Azure Synapse.

---

Key Responsibilities

1. QA & Solution Analysis

- Analyze business and technical requirements to understand data processing and validation needs.

- Participate in requirement analysis sessions and clarify functional and technical requirements with stakeholders.

- Review solution designs, data flows, mapping documents, interface specifications, and business rules.

- Validate that implemented solutions meet defined business and technical requirements.

- Identify gaps, inconsistencies, ambiguities, and potential data quality issues during requirement and solution analysis.

- Translate business requirements into detailed test scenarios, test cases, and validation conditions.

- Perform end-to-end validation of data processing workflows.

- Ensure data is accurately processed from source systems through intermediate layers to final outputs.

- Validate business rules and transformation logic implemented within data pipelines.

2. Test Planning & Execution

- Prepare comprehensive test strategies, test plans, test scenarios, and test cases for data-intensive applications.

- Execute functional, integration, regression, system, and data validation testing.

- Perform positive and negative testing for different data processing scenarios.

- Validate data pipelines across multiple environments, including staging, testing, and production.

- Identify test data requirements and prepare appropriate datasets for validation.

- Execute SQL queries to validate data processing and transformation results.

- Document test results, observations, defects, and validation evidence.

- Track testing progress and communicate status, risks, issues, and dependencies to stakeholders.

3. Data Validation & Reconciliation

- Perform detailed source-to-target data validation and reconciliation.

- Validate source, intermediate, staging, and output datasets.

- Perform record count validation between source and target systems.

- Verify data completeness, consistency, accuracy, and integrity.

- Validate data transformations against defined business rules.

- Perform field-level and record-level comparisons.

- Validate data types, formats, precision, scale, and null handling.

- Verify schema structure, layout, column names, and column sequence.

- Validate mandatory and optional fields.

- Identify missing, duplicate, truncated, or incorrectly transformed records.

- Analyze invalid records, rejected records, and exception datasets.

- Verify exception and reject-handling mechanisms.

- Compare production and staging data to identify discrepancies.

- Perform reconciliation between files, databases, and reporting layers.

- Validate data across different processing stages and identify the root cause of discrepancies.

4. File & Data Processing Validation

- Validate large-scale datasets across multiple file formats.

- Perform validation of:

 - CSV files

 - Delimited files

 - Fixed-width files

 - Excel files

 - Database tables

 - Structured and semi-structured datasets

- Validate file layouts, headers, delimiters, record formats, and column sequences.

- Verify file-level and record-level counts.

- Analyze source, intermediate, and final output files.

- Validate file-to-database and database-to-file reconciliation.

- Identify incomplete, corrupted, malformed, or invalid records.

- Verify data movement and transformation between different storage locations.

- Validate Azure-to-AWS file transfer processes.

- Ensure transferred files are complete and match the expected source datasets.

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5. Defect Investigation & Root Cause Analysis

- Investigate data discrepancies and application/data pipeline defects.

- Perform detailed root cause analysis for data quality and validation failures.

- Analyze source data, transformation logic, pipeline execution, database records, and output datasets to identify defects.

- Collaborate with developers and data engineers to resolve identified issues.

- Reproduce defects and provide detailed technical evidence.

- Perform defect impact analysis.

- Conduct retesting and regression testing after defect resolution.

- Monitor recurring data quality issues and recommend preventive solutions.

- Maintain detailed defect documentation and validation results.

---

6. Python Development & Automation

- Develop Python scripts and utilities for data validation and reconciliation.

- Design, develop, and maintain reusable data validation frameworks.

- Automate repetitive data comparison and validation activities.

- Build automated utilities for:

 - Record count validation

 - Data completeness checks

 - Schema validation

 - Column sequence validation

 - Source-to-target comparison

 - Duplicate detection

 - Exception identification

 - Data quality checks

 - Automated reporting

- Develop Python-based validation and reporting utilities.

- Optimize Python scripts for processing large datasets.

- Maintain and enhance existing automation frameworks.

- Implement reusable validation components to improve testing efficiency and coverage.

---

7. SQL Development & Data Analysis

- Write complex SQL queries for data analysis and validation.

- Perform data extraction and comparison using SQL Server / SSMS.

- Validate source and target database records.

- Perform joins, aggregations, subqueries, CTEs, and analytical queries as required.

- Develop SQL queries to identify data mismatches, duplicates, missing records, and transformation issues.

- Validate database tables, schemas, columns, constraints, and relationships.

- Perform record count and reconciliation checks using SQL.

- Analyze SQL Server metrics databases.

- Validate data processing results against expected business rules.

- Troubleshoot data discrepancies using SQL queries.

---

8. PySpark & Large-Scale Data Processing

- Develop and execute PySpark notebooks for large-scale dataset processing and validation.

- Analyze large volumes of structured and semi-structured data.

- Perform data transformation and validation using PySpark.

- Compare large source and target datasets efficiently.

- Implement data quality and reconciliation checks using PySpark.

- Analyze exception, reject, and invalid datasets.

- Optimize data validation processes for large datasets.

- Work with Azure Synapse notebooks and data processing environments.

---

9. Azure Data Factory & Pipeline Testing

- Design and execute validation scenarios for Azure Data Factory (ADF) pipelines.

- Validate pipeline execution, data movement, transformations, and dependencies.

- Monitor pipeline runs and investigate failures.

- Validate source-to-target data movement through ADF.

- Develop and maintain test pipelines using Azure Data Factory.

- Verify pipeline parameters, triggers, activities, and execution results.

- Validate file ingestion and processing workflows.

- Perform end-to-end testing of data pipelines.

- Investigate pipeline-related data discrepancies and failures.

---

10. Azure Synapse Analytics

- Work with Azure Synapse Analytics for data validation and analysis.

- Develop and execute Synapse notebooks using PySpark.

- Validate datasets processed through Synapse pipelines and notebooks.

- Perform data quality and reconciliation checks within Synapse environments.

- Analyze large-scale datasets and processing results.

- Validate data movement between Azure storage, Synapse, databases, and reporting systems.

---

11. Azure Storage & Cosmos DB

- Validate data stored in Azure Storage Accounts and Containers.

- Verify file ingestion, processing, and output data.

- Perform file-level and content-level validation within Azure storage.

- Validate data processing workflows involving Azure Storage.

- Perform data validation in Azure Cosmos DB.

- Verify records, fields, formats, and data completeness within Cosmos DB.

- Investigate discrepancies between source files, Azure storage, databases, and Cosmos DB.

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12. AWS S3 & Azure-to-AWS Validation

- Validate files stored in AWS S3.

- Perform source-to-target validation for files transferred between Azure and AWS.

- Verify file counts, file names, sizes, formats, and record counts.

- Compare source files with transferred S3 files.

- Validate data integrity after cloud-to-cloud file transfers.

- Investigate missing, incomplete, duplicate, or corrupted files.

- Support end-to-end validation of Azure-to-AWS data movement processes.

---

13. Metrics, Reporting & Power BI Validation

- Extract and validate source system metrics.

- Validate metrics stored in SQL Server databases.

- Perform reconciliation between source metrics, database metrics, and reporting outputs.

- Validate Power BI dashboards and reports against underlying source data.

- Verify report calculations, KPIs, measures, filters, and aggregations.

- Perform file-to-database-to-Power BI reconciliation.

- Validate data displayed in Power BI against SQL Server and source datasets.

- Identify discrepancies between backend data and dashboard results.

- Support reporting and analytics teams with data validation and troubleshooting.

---

14. Production Support & Job Monitoring

- Monitor scheduled data processing jobs and pipelines.

- Perform production validation and health checks.

- Analyze production failures and data discrepancies.

- Support incident investigation and resolution.

- Compare production and staging environments to identify differences.

- Validate production data after deployments and pipeline executions.

- Monitor ECG jobs and provide support for job execution and data processing issues.

- Perform post-production validation and reconciliation.

- Communicate critical production issues and risks to relevant stakeholders.

---

15. Agile Delivery & Stakeholder Collaboration

- Work effectively within an Agile/Scrum delivery environment.

- Participate in sprint planning, daily stand-ups, backlog refinement, sprint reviews, and retrospectives.

- Collaborate with Business Analysts, Developers, Data Engineers, DevOps teams, Product Owners, and other stakeholders.

- Provide timely updates on testing progress and issues.

- Participate in requirement clarification and solution discussions.

- Support release planning and production deployment activities.

- Track work items and defects using Rally.

- Ensure testing activities are aligned with sprint and release timelines.

---

Required Technical Skills

Mandatory Skills

- 5–6 years of experience in QA / Data Validation / Data Testing / Data Quality Engineering.

- Strong experience in SQL and data analysis.

- Hands-on experience with Python development and automation.

- Experience with PySpark and large-scale data processing.

- Strong experience with Azure Data Factory (ADF).

- Experience with Azure Synapse Analytics / Synapse Pipelines / Notebooks.

- Strong understanding of source-to-target data validation and reconciliation.

- Experience in data completeness, record count, schema, layout, and column validation.

- Experience in defect investigation and root cause analysis.

- Experience validating large datasets and multiple file formats.

- Experience with SQL Server / SSMS.

- Experience with Power BI dashboard/report validation.

- Strong understanding of data pipelines and ETL/ELT processes.

Cloud & Data Platform Experience

- Azure Data Factory

- Azure Synapse Analytics

- Azure Synapse Pipelines

- Azure Synapse Notebooks

- Azure Storage Accounts

- Azure Storage Containers

- Azure Cosmos DB

- Azure Privileged Identity Management (PIM)

- AWS S3

- Azure-to-AWS file transfer validation

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Preferred Skills

- Experience developing automated data validation frameworks.

- Experience building automated reporting and reconciliation utilities.

- Knowledge of ETL/ELT testing methodologies.

- Experience working with very large datasets.

- Experience in production data validation and support.

- Knowledge of cloud-based data platforms.

- Experience with Power BI data reconciliation.

- Experience working in Agile environments.

- Experience with Rally or similar Agile project management tools.

- Familiarity with Microsoft Copilot and AI-assisted productivity/automation tools.

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Key Responsibilities at a Glance

The successful candidate will be responsible for:

- Requirement analysis and clarification

- Business rule validation

- Test planning and execution

- Data quality and data validation

- Source-to-target reconciliation

- Record count and completeness validation

- Schema and layout validation

- Column sequence validation

- Exception and reject data analysis

- Production vs. staging comparison

- SQL-based data analysis

- Python automation

- PySpark development

- Azure Data Factory pipeline testing

- Azure Synapse validation

- Azure Storage validation

- Cosmos DB validation

- AWS S3 validation

- Azure-to-AWS file transfer validation

- Power BI dashboard validation

- SQL Server metrics validation

- Automated reporting

- Defect investigation and root cause analysis

- Production job monitoring and support

- Agile delivery and stakeholder collaboration

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Candidate Profile

We are looking for a detail-oriented, analytical, and technically strong QA/Data Validation professional who can work independently on complex data validation assignments.

The candidate should be comfortable working with large datasets, writing SQL queries, developing Python automation, analyzing PySpark datasets, validating cloud-based data pipelines, and troubleshooting data discrepancies across multiple systems.

Strong communication and stakeholder management skills are essential, as the role requires regular collaboration with technical and business teams.

---

Education

Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, Data Science, or a related field is preferred.

Experience

5–6 years of relevant professional experience in QA, Data Testing, Data Validation, ETL Testing, Data Quality, Data Engineering QA, or a similar role.

Location

Pan India

Employment Type

Full-Time

Keywords

QA Engineer, Data QA, Data Validation, Data Testing, ETL Testing, Data Quality, SQL, Python, PySpark, Azure Data Factory, ADF, Azure Synapse, Synapse Analytics, Synapse Pipelines, Azure Storage, Cosmos DB, AWS S3, Power BI, SQL Server, SSMS, Data Reconciliation, Source-to-Target Validation, Data Pipeline Testing, ETL QA, Automation Testing, Data Analytics, Root Cause Analysis, Agile, Rally, Cloud Data Testing, Data Engineering QA.

Job Summary

Role Overview

We are looking for an experienced Data Engineer with strong expertise in Python, ETL, Advanced SQL, CI/CD, DevOps, and Data Analytics. The ideal candidate should have hands-on experience designing and developing scalable data pipelines, transforming large datasets, and supporting data-driven applications.

Experience with Google Cloud Platform (GCP) will be an added advantage.

Key Responsibilities

• Design, develop, and maintain scalable ETL/ELT data pipelines using Python and SQL.
• Develop complex and optimized SQL queries, stored procedures, and data transformations.
• Build and maintain reliable data integration workflows across multiple data sources.
• Perform data cleansing, validation, transformation, and quality checks.
• Analyze data and provide insights to support business and technical requirements.
• Implement and maintain CI/CD pipelines for data engineering applications.
• Work with DevOps practices and tools to automate deployments, monitoring, and infrastructure processes.
• Troubleshoot data pipeline failures, performance issues, and production incidents.
• Optimize data processing workflows for performance, scalability, and reliability.
• Collaborate with Data Analysts, Data Scientists, Developers, and other stakeholders.
• Follow best practices for version control, testing, documentation, and deployment.
• Contribute to cloud-based data engineering initiatives, preferably on GCP.

Required Skills

• 5–7 years of hands-on experience in Data Engineering.
• Strong programming skills in Python.
• Strong expertise in Advanced SQL and database concepts.
• Hands-on experience with ETL/ELT processes and data pipelines.
• Good understanding of Data Warehousing and Data Modeling concepts.
• Experience with CI/CD practices and tools.
• Strong understanding of DevOps principles, automation, and deployment processes.
• Strong data analytics and problem-solving skills.
• Experience working with large datasets and performance optimization.
• Good understanding of Git/version control and software development best practices.

Good to Have

• Hands-on experience with Google Cloud Platform (GCP).
• Exposure to GCP data services such as BigQuery, Cloud Storage, Dataflow, Composer, or Pub/Sub.
• Experience with containerization/orchestration technologies such as Docker/Kubernetes.
• Experience with workflow orchestration tools such as Airflow.
• Knowledge of cloud-based data architecture and distributed data processing.

Preferred Candidate Profile

• Strong analytical and problem-solving abilities.
• Good communication and stakeholder management skills.
• Ability to work independently as well as in a collaborative team environment.
• Strong ownership of data pipelines and production systems.
• Candidates who can join at short notice are preferred.

Mandatory Skills

 Data Engineer, Python , ETL, GCP, Advanced SQL, Strong Data Analytics skills, CICD, Devops

Strong MDM Tester (Master Data Management / Data Validation / SQL Testing) Profile

2

Mandatory (Experience 1) – Must have minimum 4+ years of experience in MDM Testing, Data Testing, Database Testing, ETL Testing, or Data Quality Testing.

3

Mandatory (Experience 2) – Must have hands-on experience testing Master Data Management (MDM) systems, including validation of Customer, Product, Vendor, Material, or other master data domains.

4

Mandatory (Experience 3) – Strong hands-on experience in SQL, including writing complex queries for data validation, reconciliation, and backend database testing.

5

Mandatory (Experience 4) – Must have experience performing data migration testing, data integration testing, and end-to-end validation across multiple source and target systems.

6

Mandatory (Experience 5) – Good understanding of Data Governance, Data Quality, Data Cleansing, Duplicate Management, Matching & Survivorship Rules, and master data validation processes.

7

Mandatory (Experience 6) – Must have experience working with ETL/Data Integration tools or validating data flows between enterprise applications.

8

Mandatory (Notice Period) – Immediate joiners or candidates who can join within 2–4 weeks.

9

Mandatory (Note) - The position is open across all Cognizant offices pan India. Candidates must be willing to attend the F2F interview at the nearest Cognizant office location.

10

Preferred (Platform) – Exposure to Informatica MDM, SAP MDG, Reltio, Semarchy, IBM InfoSphere MDM, Stibo, or similar MDM platforms.

Job Description

We are looking for an experienced Data Engineer with strong expertise in Python, ETL, SQL, CI/CD, and DevOps to design, develop, and maintain scalable data pipelines and data processing solutions.

Key Responsibilities

• Design, develop, and maintain scalable ETL/ELT data pipelines.
• Develop data processing solutions using Python.
• Write complex and optimized SQL queries, stored procedures, and data transformations.
• Build and maintain data ingestion and integration workflows.
• Implement data quality, validation, monitoring, and error-handling processes.
• Develop and maintain CI/CD pipelines for data engineering applications.
• Work with DevOps tools and practices for automated build, deployment, and infrastructure management.
• Collaborate with data analysts, data scientists, software engineers, and business teams.
• Optimize data pipelines for performance, reliability, and scalability.
• Troubleshoot production data issues and ensure timely resolution.
• Follow best practices for version control, code quality, testing, and deployment.

Mandatory Skills

• Python
• ETL
• SQL
• CI/CD
• DevOps
• Git / Version Control
• Strong problem-solving and debugging skills

Strong Senior Developer – PL/SQL, SQL & ETL (Microsoft SSIS) Profile

2

Mandatory (Experience 1) – Must have minimum 5+ years of strong hands-on experience in PL/SQL and SQL development, including complex stored procedures, functions, queries, joins, data manipulation, and query/performance optimization.

3

Mandatory (Experience 2) – Must have strong hands-on experience in ETL development using Microsoft SSIS, including building, maintaining, optimizing, and troubleshooting SSIS packages for large-volume data movement and transformation.

4

Mandatory (Experience 3) – Must have solid experience working with Data Warehousing concepts and architectures, including data models, fact/dimension structures, ETL data flows, and enterprise reporting/data warehouse environments.

5

Mandatory (Experience 4) – Must have experience managing batch jobs, scheduling, and data pipelines, ensuring timely and reliable execution of enterprise ETL workflows.

6

Mandatory (Experience 5) – Must have hands-on experience in production support for SSIS/ETL and data warehouse jobs, including monitoring job execution, troubleshooting failures, performing root cause analysis, and implementing preventive fixes.

7

Mandatory (Experience 6) – Must have experience with data quality, validation, and troubleshooting, including identifying and resolving data discrepancies/issues affecting downstream reports, dashboards, and analytics.

8

Mandatory (Experience 7) – Must have experience with unit, integration, and regression testing of SQL, PL/SQL, and ETL components, along with strong documentation of technical designs, data mappings, data flows, and deployment processes.

9

Mandatory (Location) – Must be willing to work in a hybrid model from a city where Cognizant has an office.

10

Mandatory (Notice Period) – Immediate joiners or candidates who can join within 2–4 weeks.

Role Summary

We are hiring a Data Engineer / ML Data Pipeline Engineer to build and operate the data backbone of the Enterprise AI platform:

What You'll Own

• Ingestion & ETL/ELT pipelines for heterogeneous project folders (PDF drawings, SVG files, IFC models, BBS.json bar-bending-schedule data, Excel exports, and AI agent output JSON).
• AWS-based data architecture: S3 raw/staging/curated/outputs structuring, partitioning, versioning, and lifecycle management; querying via Athena/Glue and warehousing via Redshift or Snowflake as needed.
• Data validation frameworks: GUID cross-referencing between SVG and BBS data, schema enforcement, duplicate/orphan detection, reference integrity checks, and structured validation reporting.
• Agent run logging & observability: designing the database schema and pipelines that track every AI agent run (inputs, outputs, status, errors, cost, retries, reviewer feedback).
• AI Factory monitoring dashboards: operational dashboards (failure rates, retries, latency, data quality) and business dashboards (throughput, cost per run, rework rate) for Power BI/QuickSight or equivalent.
• ML data pipeline support: dataset preparation, labeling/annotation workflows, human-in-the-loop review tooling, and dataset versioning for models that classify or QC drawing issues.
• APIs: designing and building FastAPI/Flask endpoints to trigger validation runs and expose agent processing status to internal tools.
• Data quality & testing discipline: idempotent pipelines, quarantine/reject handling, regression and reconciliation testing, and root-cause debugging when pipelines or query performance degrade in production.

Key Skills — Non-Negotiable (Must-Have, Strong Level)

• Python — production-grade scripting: file/folder handling, JSON/schema processing, clean error handling, not just notebook-level scripting.
• SQL — strong hands-on ability, including GROUP BY/HAVING for duplicate detection, window functions, and daily aggregate/rate calculations (e.g., success-rate queries).
• AWS S3 data handling — practical experience structuring buckets for raw/staging/curated data, versioning, and avoiding overwrite issues at scale.
• Data validation — demonstrable experience building validation logic (set comparisons, duplicate/missing detection, structured pass/fail reporting), not just "I write assertions."
• ETL/ELT pipeline design — end-to-end ownership of at least one pipeline: source → transform → storage → validation → monitoring → business outcome, with clear articulation of what they personally built.
• Query/warehouse engine judgment — working knowledge of when to use Athena vs. Redshift vs. Snowflake (or equivalent), partitioning, clustering, sort/distribution keys, and storage format trade-offs (Parquet vs. JSON vs. CSV).

Key Skills — Good to Have

• Dashboarding — Power BI / QuickSight (or equivalent) fact/dimension table design, KPI cards, drill-downs; medium-to-strong level is a plus but trainable.
• FastAPI / Flask — building real endpoints with request/response schemas and basic error handling; especially valuable for validation-trigger and agent-status APIs.
• ML data pipeline experience — dataset labeling, annotation platform design, train/test/validation splitting, dataset versioning; strong on the pipeline/data side rather than model training itself.
• Human-in-the-loop / review tooling — experience building or contributing to browser-based labeling/review platforms (session persistence, label schema, export formats).
• Large-scale metadata querying — experience making file discovery fast across large volumes (1,000+ projects, thousands of files each) via metadata index tables, event-based ingestion, or catalog tools like AWS Glue.

Position title: Business Intelligence and Data Analyst

Summary -

• Operate and continuously improve the CARS/BI backend within the global Enterprise Data and Business Intelligence platform, ensuring system stability, continuous data loading, and Warehouse the timely delivery of business-critical reports.

• Own the underlying data foundation — SQL pipelines, relational data models, and data warehouse structures — that internal reporting teams and global Data Management initiatives depend on, and act as 1st and 2nd level support for internal customers such as SCM and Sales.

• The role is needed to secure daily BI availability in the Asian time zone, reduce the risk of late or incorrect reporting, and add dedicated database and ETL capacity to the global BI team, including support to the Data Centre team on infrastructure-related issues.

Responsibilities:

• Operate, monitor, and maintain the CARS/BI data integration and data processing workflows, ensuring continuous data loading and stable daily business operations.

• Provide 1st and 2nd level support for CARS/BI, including user support, incident handling, and root-cause analysis of data issues.

• Develop, maintain, and optimise SQL-based data pipelines and transformations within the Enterprise Data Warehouse.

• Design and maintain relational data models and data warehouse structures for CARS BI.

• Integrate data from ERP and other enterprise systems into the BI backend.

• Ensure data consistency, integrity, and performance at the database level, including query tuning and database efficiency improvements.

• Administer the BI technical environment and contribute to its continuous enhancement as part of a global team.

• Support reporting teams by providing structured, reliable datasets and safeguarding the timely delivery of business-critical reports.

• Contribute to global Data Management initiatives, with a focus on Master Data Management and the development of a Common Data Model within the Business Integration Platform.

• Collaborate with the Data Centre team to resolve infrastructure-related issues affecting BI availability.

Education and Experience:

Bachelor's degree in Engineering (BE/B.Tech) or equivalent in Computer Science, Information Technology, or a comparable technical field.

Minimum 3 years of relevant professional experience in Business Intelligence, data warehousing, or database development, including hands-on SQL and ETL work in an enterprise environment.

Experience supporting business users in a global or multi-time-zone IT organisation is an advantage.

Competencies:

• Knowledge about either BI platforms (e.g. IBM Cognos BI suite) or ETL tools (e.g. Informatica PowerCenter)

• Database, data modeling and SQL (preferred Oracle PLSQL)

• Good analytical and communication skills

• Teamplayer

• English

• Strong hands-on experience with SQL (advanced level)

• Solid understanding of relational databases and data warehouse concepts

• Experience with ETL tools and database technologies (e.g., Oracle, SQL Server, SAP BW,Informatica or similar)

• Performance tuning and query optimization skills

• Structured, detail-oriented, and quality-focused working style

• Good understanding of enterprise data flows (SAP/CARS is a plus)

Key Interfaces and Stakeholders:

Only internal customers with different topics: e.g. SCM, Sales etc.

Geography to cover and Travel requirements:

Asian Time Zone, Sometimes travel is required

Behavioral Characteristics

Reliability and accountability — the role safeguards daily reporting availability, so dependable ownership of monitoring and issue follow-up is expected. Structured, analytical problem solving with the patience for thorough root-cause analysis. Service orientation and clear communication towards internal customers such as SCM and Sales. Initiative taking and self-reliance, given largely independent work in the Asian time zone. Cooperation and team spirit within a globally distributed BI team across cultures and time zones. Flexibility and resilience under time pressure, including occasional off-hours support during critical data loads. Integrity and discretion when handling confidential business data. Quality focus and attention to detail, with a continuous improvement mindset.

Interview process

2 rounds - Virtual interview and 1 round Face to Face

Any other Criteria

• Notice Period: Below 60 or 90 Days . No Negotiation on Notice Period
• Gender: Female and Male; Female preferred
• Qualification:Bachelor's degree in Engineering (BE/B.Tech) or equivalent in Computer Science, Information Technology,