QA Engineer

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.

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 Title: Senior Data Tester

Location : Hyderabad

Mode: Hybrid

Notice Period: Immediate Joiner

Key Responsibilities:

• 8+ years of experience in ETL/data testing.
• Design, implement, and execute data validation test plans and test cases.
• Understanding of data modelling and data governance principles.
• Experience with test automation frameworks and scripting (e.g., Python, Shell)Conduct thorough ETL testing, including data extraction, transformation, and loading.
• Validate data integrity across various sources and destinations (data lakes, warehouses, etc.)
• Perform data reconciliation and analysis to identify inconsistencies or data quality issues.
• Develop and maintain automated data testing frameworks using SQL or scripting languages.
• Strong experience with SQL and writing complex queries for data validation.
• Knowledge of data warehouse concepts and testing tools. Experience with ETL tools (e.g., Informatica, Talend, SSIS, etc.)
• Familiarity with cloud platforms (Azure, GCP) and modern data tools (e.g., Snowflake, Big Query).
• GCP is mandatory. Experience in Agile development and working within cross-functional teams.
• Exposure to BI tools (Power BI, Tableau, Looker)
• Familiarity with CI/CD pipelines and version control systems like Git ISTQB or equivalent testing certifications.

Job Title: Senior QA Automation Engineer (Healthcare

Data & Cloud)

Experience Level: 4–8 Years

Location: [Bangalore]

Tech Stack: Java, REST/SOAP, AWS, NEMSIS Standards

Role Overview

As a Senior QA Automation Engineer for our NEMSIS implementation, you will be responsible

for ensuring the seamless exchange of Emergency Medical Services (EMS) data. You will

design and execute sophisticated automation frameworks that handle both legacy SOAP

services and modern RESTful microservices, all hosted within an AWS ecosystem.

The role is not just checking UI applications but ensuring that life-critical data is accurate,

secure, and compliant with national standards.

Key Responsibilities

● Framework Development: Build and maintain scalable, Java-based automation

frameworks UI-Automation (TestNG), API and backend validation.

● Hybrid API Testing: Develop automated suites to validate both SOAP (XML-heavy) and

REST (JSON) endpoints.

● NEMSIS Compliance: Validate data against NEMSIS Schematron rules and XSDs to

ensure 100% compliance with EMS data standards.

● Cloud Integration: Leverage AWS services (e.g., Lambda, S3, CloudWatch) to

execute tests and monitor system health.

● Data Integrity: Perform deep-dive database testing and XML/JSON parsing to ensure

data remains uncorrupted across the pipeline.

● CI/CD Ownership: Integrate automated suites into Bitbucket CI pipelines for continuous

feedback.

Technical Qualifications

Category Requirements

Core Language Strong proficiency in Java (Collections, Exceptions, Multi-threading).

API Testing Advanced experience with RestAssured, SoapUI, or Postman for

automated API testing.

Data Formats Expert-level knowledge of XML, XSD, and JSON validation.

Cloud Hands-on experience testing applications hosted on AWS.

Tools Proficiency with Maven, Git, and Jira.

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.

---

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.

---

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

---

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.

---

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

---

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.

Quality Assurance Engineer

Location: Remote

Experience: 9–12 Years

Notice Period: Immediate Joiner only

JD:

API Testing: Postman/Swagger

Test Management & Defect Tracking: Jira, Test Director, TFS, Quality Center

Database Testing: SQL (Query Writing & Validation)

Domain Knowledge: US Healthcare Systems, HIPAA, HL7

Methodology: Agile (Scrum)

Strong QA Engineer Profile with manual + automation testing across web, API, and AI-driven features

2

Mandatory (Experience 1): Must have 2+ years in QA / software testing, covering both manual and automation testing

3

Mandatory (Experience 2): Must have experience designing, writing, and maintaining test plans, test cases, and test data for web, API, and AI-driven features

4

Mandatory (Experience 3): Must have experience performing functional, regression, integration, and exploratory testing across releases

5

Mandatory (Tech skill 1): Must have hands-on automation experience with a framework such as Selenium, Playwright, Cypress, or similar

6

Mandatory (Tech skill 2): Experience testing AI/ML or data products (validating accuracy, consistency, edge cases, bias, graceful failure handling)

7

Mandatory (Tech skill 3): Must have API testing experience with tools like Postman, REST Assured, or equivalent, and experience testing REST APIs and data pipelines

8

Mandatory (Tech skill 4): Must have working knowledge of Python, JavaScript, or Java for test automation

9

Mandatory (Tech skill 5): Must have SQL and data skills — able to write queries to validate data and back-end behaviour

10

Mandatory (Skill 1): Must have a strong grasp of QA methodologies, SDLC/STLC, and Agile/Scrum practices.

11

Mandatory (Skill 2): Must have an analytical mindset with sharp attention to detail and clear defect communication

12

Mandatory (Skill 3): Must have strong communication skills

13

Preferred (CI/CD): Experience integrating automated tests into CI/CD pipelines (Jenkins, GitHub Actions)

14

Preferred (Domain): Cloud/AWS and insurance domain exposure

Strong QA Engineer Profile with manual + automation testing across web, API, and AI-driven features

2

Mandatory (Experience 1): Must have 2+ years in QA / software testing, covering both manual and automation testing

3

Mandatory (Experience 2): Must have experience designing, writing, and maintaining test plans, test cases, and test data for web, API, and AI-driven features

4

Mandatory (Experience 3): Must have experience performing functional, regression, integration, and exploratory testing across releases

5

Mandatory (Tech skill 1): Must have hands-on automation experience with a framework such as Selenium, Playwright, Cypress, or similar

6

Mandatory (Tech skill 2): Experience testing AI/ML or data products (validating accuracy, consistency, edge cases, bias, graceful failure handling)

7

Mandatory (Tech skill 3): Must have API testing experience with tools like Postman, REST Assured, or equivalent, and experience testing REST APIs and data pipelines

8

Mandatory (Tech skill 4): Must have working knowledge of Python, JavaScript, or Java for test automation

9

Mandatory (Tech skill 5): Must have SQL and data skills — able to write queries to validate data and back-end behaviour

10

Mandatory (Skill 1): Must have a strong grasp of QA methodologies, SDLC/STLC, and Agile/Scrum practices.

11

Mandatory (Skill 2): Must have an analytical mindset with sharp attention to detail and clear defect communication

12

Mandatory (Skill 3): Must have strong communication skills

13

Preferred (CI/CD): Experience integrating automated tests into CI/CD pipelines (Jenkins, GitHub Actions)

14

Preferred (Domain): Cloud/AWS and insurance domain exposure

Palcode.ai is hiring a Software Developer, Tester – QA Manual Tester

Role Description

We are seeking a QA Engineer for remote role with a 6 month - 12 month. The QA Engineer will be responsible for executing manual tests, drafting automation testing, creating precise test cases, and ensuring the quality of software deliverables. The role involves collaborating with the development team to identify, report, and resolve bugs while contributing to maintaining a high standard of software testing and quality assurance.

Qualifications

• Experience in Test Execution and identifying potential issues effectively.
• Proficiency in Quality Assurance processes with a keen eye for detail and accuracy.
• Skills in Manual Testing and creating comprehensive Test Cases.
• Knowledge of software development workflows and expertise in Software Testing.
• Strong analytical and problem-solving abilities.
• Ability to work independently in a remote team environment.
• Bachelor’s degree in Computer Science, Engineering, or a related field is preferred.
• Familiarity with AI-driven tools and construction software is a plus.

About Us

We believe the future of software development is AI-native — where engineers operate at a higher level of abstraction and quality remains non-negotiable. 

Incubyte is a software craft consultancy where the “how” of building software matters as much as the “what”.  

We partner with companies of all sizes, from helping enterprises build, scale, and modernize to early-stage founders bring their ideas to life. 

Our engineers operate in an AI-native development model, using AI as a collaborator across the SDLC to accelerate development while upholding the discipline of software craftsmanship. Guided by Software Craftsmanship and Extreme Programming practices, we build reliable, maintainable, and scalable systems with speed, without compromising quality. If this way of building software resonates with you, we’d like to talk. 

Our Guiding Principles 

These principles define how we work at Incubyte. They are non-negotiable. 

Relentless Pursuit of Quality with Pragmatism 

  We build high-quality systems without losing sight of delivery. 

Extreme Ownership 

  We take responsibility end-to-end for decisions, execution, and outcomes. 

Proactive Collaboration 

  We collaborate closely, challenge each other, and solve problems together. 

Active Pursuit of Mastery 

  We continuously improve our craft and raise our bar. 

Invite, Give, and Act on Feedback 

We seek, give, and act on feedback to get better every day. 

Ensuring Client Success 

We act as trusted partners and focus on real outcomes, not just output. 

Job Description

This is a remote position.

Experience Level

2+ years of experience in SQL, Python, and Snowflake (or equivalent cloud data warehouse), Azure Cloud services.

Role Overview

If you're a Data Craftsperson who takes pride in clean, well-tested data solutions and believes in the principles of Extreme Programming, we'd love to meet you. At Incubyte, we're a DevOps organization where developers own the entire release cycle — you'll get hands-on experience across data engineering, analytics, cloud infrastructure, and direct client communication. This role sits primarily in data engineering (80%) with a meaningful analytics component (20%), supporting our client's data systems end-to-end.

What You'll Do

• Design, build, and maintain data pipelines and infrastructure using SQL and Python
• Work within Snowflake to build and optimize data models supporting business use cases
• Parse and process structured and semi-structured data (JSON, XML) from varied sources
• Diagnose issues across raw, intermediate, and summary tables
• Build SQL queries to support repeatable analytics use cases based on stakeholder requirements
• Investigate and resolve data quality issues, including time-sensitive or urgent ones
• Identify opportunities to consolidate models and maintain a single source of truth (SSOT)

Requirements

What We're Looking For

• 2+ years of experience with SQL and relational databases, with the ability to understand complex data relationships and transformations (required)
• 2+ years of experience with Python for data engineering tasks (required)
• Experience with Snowflake or an equivalent cloud data warehouse (required)
• Experience parsing JSON and XML data (a plus)
• A strong eye for data quality and attention to detail
• Knowledge of Git (required)
• Knowledge of Azure cloud services such as Azure Data Factory, Azure Blob Storage, and Azure SQL Database (required)
• Knowledge of data infrastructure/modeling tools like DBT, Fivetran (a plus)
• Experience with BI tools like Power BI(a plus, not core to this role)
• Knowledge of Docker, Linux, Shell/Bash, and virtualization technologies (a plus)
• Knowledge of SSIS packages (a plus)
• Familiarity with CI/CD methodologies

Benefits

Life at Incubyte  

We are a remote-first company with structured flexibility. Teams commit to shared rhythms during core hours, ensuring smooth collaboration while maintaining autonomy. Twice a year, we come together in person for a co-working sprint and once a year for a retreat - with all travel expenses covered. 

Our environment is built for crafters: pairing, refactoring, experimenting with AI, and pushing the boundaries of software excellence. We are all lifelong learners, and our work is our passion. 

Perks

• Dedicated learning & development budget. 
• Sponsorship for conference talks. 
• Comprehensive medical & term insurance.
• Employee-friendly leave policies. 
• Home Office fund 
• Medical Insurance