Senior Data engineer

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

GCP Data Engineering Lead

Experience: 9+ Years

Lead Experience: 2+ Years

Location: Bangalore / Hyderabad

Key Skills:

• Strong experience in GCP Data Engineering and BigQuery
• Hands-on experience with Oracle Exadata / PL-SQL
• Strong knowledge of PySpark / Scala and Python
• Experience with GoldenGate, Kafka and CDC
• Hands-on experience with Apache Airflow
• Good experience in CI/CD and DevOps practices
• Experience with Terraform / Infrastructure as Code
• Exposure to AI/LLM technologies and GenAI solutions
• Strong understanding of data architecture, ETL/ELT and data pipelines

Roles & Responsibilities:

• Lead the design and development of scalable GCP data engineering solutions.
• Design and implement batch and real-time data pipelines using BigQuery, PySpark, Kafka/CDC and Airflow.
• Work with Oracle Exadata/PL-SQL and GoldenGate for data integration and migration.
• Implement CI/CD pipelines and infrastructure automation using Terraform.
• Explore and integrate AI/LLM capabilities into data engineering solutions.
• Lead technical discussions, code reviews, solution design and mentor team members.
• Collaborate with business and technical teams to deliver high-quality data solutions.

Technical Lead 

Job Description 

Role Overview 

We are seeking a highly skilled Data Engineering Lead with minimum of 5 years of hands-on experience in Azure data engineering, data warehousing, and automation-driven  integration. The ideal candidate should be a proven technical lead, capable of driving end-to-end 

project delivery and working closely with customer teams. This is a Work from Office / Customer Site role. 

Roles and Responsibilities 

• Lead the design, development, and delivery of data engineering and automation projects. 

• Architect and implement ETL/ELT pipelines using Azure Data Factory. 

• Design and manage enterprise data warehouses including dimensional modeling and 

schema optimization. 

• Manage Azure components including Storage Accounts, Data Lakes, Azure SQL, and Synapse. 

• Drive automation initiatives across data ingestion and transformation workflows. 

• Collaborate with business and technical stakeholders to translate requirements into scalable solutions. 

• Mentor team members and enforce engineering best practices. 

• Serve as the technical anchor responsible for ensuring high-quality, on-time delivery. 

Skills and Qualification 

• Strong hands-on experience with Azure Data Factory, Azure Storage, Azure SQL/Synapse. 

• Deep understanding of data warehousing concepts: star/snowflake schemas, fact/dimension modeling. 

• Experience with automation-led data engineering solutions. 

• Strong troubleshooting, optimization, and analytical skills. 

• Excellent communication and stakeholder management abilities. 

• Proven experience as a Technical Lead leading teams and delivery. 

Must Have 

• Min of 5 years of relevant data engineering experience. 

• Strong Azure Data Engineering and Data Warehousing expertise. 

• Proven Technical Lead experience. 

• Ability to work from office and customer site. 

• Strong ownership mindset with a focus on quality and delivery excellence.

Job Title : Tech Lead – Data Lake Platform

Number of Positions : 2

Experience : 7+ Years

Role Type : Technical Lead / Data Platform Lead

Domain : Data Engineering / Data Platform / AWS

About the Role :

We are looking for an experienced Tech Lead – Data Lake Platform to lead the design, development, and operations of an enterprise-scale AWS-based Data Lake Platform.

The platform will ingest data from multiple business systems, process it through structured data layers, and serve data for analytics, reporting, APIs, and operational applications.

As a Tech Lead, you will be responsible for setting the technical direction, leading data engineering teams, driving platform reliability and performance, and owning the platform's delivery, governance, and production support end to end.

Core Tech Stack :

AWS | S3 | EMR | Glue | Athena | Redshift | DMS | Lambda | RDS | IAM | Airflow | Spark/PySpark | SQL | Data Modeling | Hasura | GraphQL | DBT | PostgreSQL/Aurora | Kafka

Key Responsibilities :

• Own the overall Data Lake Platform architecture, covering data ingestion, staging, curated layers, consumption, analytics, and API/data serving.
• Lead the design and development of production-grade data pipelines using AWS Glue, EMR/Spark, Airflow, DBT, Athena, and Redshift.
• Design scalable batch and analytics pipelines with a focus on reliability, performance, data quality, and maintainability.
• Own the API/data-serving architecture from consumption data → RDS/PostgreSQL → Hasura GraphQL → Lambda/API Gateway.
• Drive improvements in platform stability, including orchestration failures, cluster sizing, pipeline SLAs, query performance, and production reliability.
• Design and implement appropriate AWS security, access control, IAM, PII handling, and data governance practices.
• Lead technical discussions, architecture decisions, code reviews, and engineering best practices.
• Mentor and guide data engineers while ensuring high-quality and scalable engineering delivery.
• Own production support, incident management, troubleshooting, and root-cause analysis (RCA) for critical data platform issues.
• Develop and maintain runbooks, operational procedures, monitoring, and incident response practices.
• Collaborate with business, product, application, and analytics teams to onboard new datasets and support reporting, API, and data consumption requirements.
• Ensure the platform meets defined availability, performance, security, data quality, and compliance requirements.

Must-Have Skills :

• 7+ years of experience in Data Engineering, Data Platform Engineering, or related areas, including experience in technical leadership or architecture.
• Strong hands-on experience with AWS Data Services, including:
• Amazon S3
• AWS EMR
• AWS Glue
• Amazon Athena
• Amazon Redshift
• AWS DMS
• AWS Lambda
• Amazon RDS
• AWS IAM
• Strong production experience with Apache Airflow.
• Strong hands-on experience with Apache Spark / PySpark.
• Strong SQL skills and experience with data modeling, including layered data architecture, data marts, and enterprise data models.
• Experience with Hasura or a similar GraphQL layer for PostgreSQL-based data/API serving.
• Strong understanding of data lake architecture and enterprise data platforms.
• Proven experience leading engineers and driving technical decisions.
• Hands-on experience with production support, troubleshooting, incident management, and RCA.
• Strong understanding of data platform performance, scalability, reliability, and SLA management.

Good-to-Have Skills :

• Experience with DBT and modern data transformation practices.
• Experience with lakehouse table formats on Amazon S3, such as Apache Iceberg or similar technologies.
• Strong knowledge of Amazon Redshift workload optimization, including :
• Distribution keys
• Sort keys
• Spectrum
• External tables
• Experience with Kafka or other streaming/data ingestion technologies.
• Experience with PostgreSQL / Amazon Aurora as a data-serving layer.
• Experience with Lambda and API Gateway for API-based data serving.
• Experience with enterprise data governance, data quality, security, and compliance.
• Experience in BFSI / Banking / Financial Services / Insurance domain.

Ideal Candidate :

The ideal candidate is a hands-on Data Platform / Data Engineering Lead who can operate at both the architecture and implementation level. You should be comfortable designing an AWS Data Lake from end to end, leading engineers, troubleshooting production issues, and working closely with business and application teams to deliver reliable data products.

Job Summary

The Technical Lead will be responsible for overseeing and leading projects related to Azure Data Factory (ADF), Azure Databricks, SQL, Oracle PL/SQL, and Python. The role involves designing, developing, and implementing data solutions while ensuring they meet the business requirements and align with best practices. (1.) Key Responsibilities

1. Lead and manage end-to-end data engineering projects using azure data factory, azure databricks, sql, oracle pl/sql, and python.

2. Collaborate with stakeholders to gather and understand requirements for data pipelines and analytics solutions.

3. Design and develop etl processes, data models, and data integration solutions.

4. Provide technical guidance and mentorship to the team members.

5. Ensure data quality, data governance, and data security standards are maintained throughout the project lifecycle.

6. Troubleshoot and optimize data pipelines and processes for performance and efficiency.

7. Stay updated on the latest trends and technologies in data engineering and contribute to continuous improvement efforts.

Skill Requirements

1. Proficiency in azure data factory (adf) and azure databricks for building and managing data pipelines.

2. Strong experience with sql and oracle pl/sql for data querying and manipulation.

3. Advanced programming skills in python for scripting and data processing tasks.

4. Knowledge of data modeling, data warehousing concepts, and database design principles.

5. Ability to work in a collaborative team environment and communicate effectively with stakeholders.

6. Strong analytical and problem-solving skills with attention to detail.

7. Experience in data visualization tools and techniques is a plus.

Certifications: Relevant certifications in Azure Data Factory, Azure Databricks, SQL, Oracle PL/SQL, or Python are advantageous.

Skill (Primary)

Data Fabric-Azure-Azure Data Factory (ADF)

Job Summary: GCP Data Engineering Lead

Experience: 9+ Years

Location: Bangalore / Hyderabad

Notice Period: Immediate to 15 Days

Key Skills:

• Strong experience in GCP Data Engineering
• Proven Technical Lead / Lead experience
• Strong programming skills in Python
• Hands-on experience with PySpark
• Strong expertise in SQL / PL-SQL
• Good understanding of GCP data services and data engineering concepts
• Experience in designing and developing scalable data pipelines
• Strong problem-solving and technical leadership skills

Roles & Responsibilities:

• Lead the design and development of scalable GCP data engineering solutions.
• Develop and optimize data pipelines using Python, PySpark and SQL/PL-SQL.
• Design data processing solutions and ensure performance and scalability.
• Provide technical leadership, conduct code reviews, and mentor team members.
• Collaborate with business and technical teams to understand requirements and deliver data solutions.
• Troubleshoot issues and ensure quality across the data engineering lifecycle.

Data Engineer – Microsoft Fabric

Location: Pune, India

Work Mode: Hybrid

Experience: 6+ Years

Employment Type: Full-time contactor

Compensation: As per market standards, commensurate with experience and expertise

Shift Timings: 2:00 PM – 11:00 PM IST

Notice Period: 0 – 15 days

About the Role

Jade Business Services (JBS) is seeking a Data Engineer – Microsoft Fabric to join our Pune team and work on enterprise-scale data transformation and analytics initiatives.

We are looking for a hands-on Data Engineer with strong experience in Microsoft Fabric, SQL, Python/PySpark and modern data engineering practices. The candidate will be responsible for building scalable data pipelines, implementing Lakehouse and Warehouse solutions, developing data models and supporting governed, reliable and AI-ready data platforms.

The ideal candidate should be comfortable working with architects, engineering teams and client stakeholders to translate business requirements into scalable and production-ready data solutions.

Roles and Responsibilities

• Design and develop data solutions using Microsoft Fabric, including OneLake, Lakehouse, Warehouse and Data Factory pipelines.
• Build and maintain scalable ETL/ELT pipelines for batch and incremental data processing.
• Develop data ingestion and transformation pipelines using Fabric Data Factory, SQL, Python and/or PySpark.
• Implement Medallion Architecture using Bronze, Silver and Gold layers.
• Work with Lakehouse and Fabric Warehouse for enterprise data processing and analytics.
• Develop and maintain data models, tables, views and optimized SQL queries.
• Build and support semantic models for Power BI and analytical workloads.
• Implement data quality, validation, monitoring and error-handling mechanisms.
• Work with metadata, lineage and governance requirements using Microsoft Purview.
• Implement data security, access controls and role-based permissions across data platforms.
• Support Data Product and domain-oriented data architecture principles.
• Follow DataOps practices including CI/CD, deployment, monitoring and production support.
• Troubleshoot pipeline failures, performance issues and data quality problems.
• Optimize data pipelines, queries and storage for performance and cost efficiency.
• Work closely with Data Architects and business stakeholders to understand requirements and implement technical solutions.
• Participate in technical design discussions, code reviews and architecture reviews.
• Maintain technical documentation, data flow diagrams and pipeline documentation.
• Support production deployments, incident resolution and SLA-driven data platform operations.
• Identify opportunities for automation and AI-assisted improvements across data engineering processes.

Qualifications and Skills

• 6+ years of experience in Data Engineering, Data Integration or Data Platform development.
• Strong hands-on experience with Microsoft Fabric.
• Experience with:
• Microsoft Fabric Lakehouse
• Fabric Warehouse
• OneLake
• Fabric Data Factory / Pipelines
• Semantic Models
• Strong understanding of Lakehouse and Medallion Architecture.
• Strong SQL development and query optimization skills.
• Hands-on experience with Python and/or PySpark.
• Experience developing enterprise ETL/ELT and data integration pipelines.
• Experience with batch and incremental data processing.
• Understanding of data modelling concepts including dimensional modelling.
• Knowledge of data quality, metadata, lineage and data governance.
• Working knowledge of Microsoft Purview.
• Understanding of Data Mesh and Data Product concepts.
• Experience with CI/CD, version control, monitoring and DataOps practices.
• Understanding of cloud security, access controls and data privacy.
• Good troubleshooting and problem-solving skills.
• Strong communication skills and ability to work with distributed and client-facing teams.

Preferred Skills

• Microsoft Fabric or Azure Data certifications.
• Experience migrating workloads from Azure Synapse, SQL Server, Databricks or other data platforms to Microsoft Fabric.
• Experience implementing Medallion Architecture on Microsoft Fabric.
• Experience with Power BI and semantic modelling.
• Exposure to AI/ML, Generative AI or Agentic AI use cases on enterprise data platforms.
• Experience working with Data Products or domain-oriented data solutions.
• Experience in Energy & Utilities, Healthcare, Financial Services or Insurance.
• Experience working with US or international enterprise clients.

What We Expect

The ideal candidate should be hands-on first and capable of independently building, troubleshooting and optimizing Fabric data solutions. You should be able to explain the technical decisions behind your implementation and work effectively with architects and engineering teams to deliver production-ready solutions.

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

About Us:

The QX Impact was launched with a mission to make A.I accessible and affordable and deliver AI Products/Solutions at scale for the enterprises by bringing the power of Data, AI, and Engineering to drive digital transformation. We believe without insights; businesses will continue to face challenges to better understand their customers and even lose them. Secondly, without insights businesses won't’ be able to deliver differentiated products/services; and finally, without insights, businesses can’t achieve a new level of “Operational Excellence” is crucial to remain competitive, meeting rising customer expectations, expanding markets, and digitalization.

Job Summary:

We are looking for a Senior Data Engineer who is creative, collaborative, and adaptable to join our agile team of data scientists, engineers, and UX developers. The role focuses on building and maintaining robust data pipelines to support advanced analytics, data science, and BI solutions.

As a Senior Data Engineer, you will work with internal and external data, collaborate with data scientists, and contribute to the design, development, and deployment of innovative solutions.

Key Responsibilities:

• Design, develop, test, and maintain optimal data pipeline and ETL architectures.
• Map out data systems and define/design required integrations, ETL, BI, and AI systems/processes.
• Prepare and optimize data for predictive and prescriptive modeling.
• Collaborate with teams to integrate ERP data into the enterprise data lake, ensuring seamless flow and quality.
• Enhance cloud data infrastructure on AWS or Azure for scalability and performance.
• Utilize big data tools and frameworks to optimize data acquisition and preparation.
• Build architectures to move data to/from data lakes and data warehouses for advanced analytics.
• Develop and curate data models for analytics, dashboards, and reports.
• Conduct code reviews, maintain production-level code, and implement testing approaches.
• Monitor, troubleshoot, and resolve data ingestion workflows to maintain reliability and uptime.
• Drive innovation and implement efficient new approaches to data engineering tasks.

Must-Have Skills:

• Bachelor’s degree in Computer Science, Mathematics, Engineering, or a related field.
• 5+ years of experience working with enterprise data platforms, including building and managing data lakes.
• 3–5 years of experience designing and implementing data warehouse solutions.
• Expertise in SQL, including developing stored procedures (SP) and applying advanced data design concepts.
• Proficiency in Spark (Python/Scala) and Spark Streaming for real-time data pipelines.
• Experience with AWS or Azure services (e.g., AWS Glue, Azure Data Factory, Redshift, Snowflake).
• Familiarity with big data tools such as Apache Kafka, Apache Spark, or Flink.
• Hands-on experience with orchestration tools (e.g., Apache Airflow, Prefect).
• Knowledge of CI/CD processes, version control (e.g., Git, Jenkins), and deployment automation.
• Strong problem-solving, communication, and collaboration skills.

Good-to-Have Skills:

• Experience in integrating ERP data into data lakes.
• Experience with traditional ETL tools (e.g., Talend, Pentaho).

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!

Role Overview

We are looking for a GCP Data Engineer with 10+ years of experience to design, develop, and optimize scalable cloud-based data solutions. The ideal candidate will have strong hands-on expertise in GCP, BigQuery, and advanced SQL, with experience building data pipelines and working with large-scale datasets.

Key Responsibilities

• Design and develop scalable data pipelines and ETL/ELT processes on GCP.
• Build, optimize, and maintain data solutions using Google BigQuery.
• Develop complex SQL queries for data transformation, aggregation, and analysis.
• Design efficient data models and optimize pipelines for performance, scalability, and cost.
• Integrate data from multiple sources and ensure data quality, reliability, and availability.
• Troubleshoot pipeline and data issues and drive continuous improvement.
• Collaborate with data architects, analysts, application teams, and business stakeholders.
• Follow best practices for cloud security, data governance, testing, and documentation.

Required Skills

• 8+ years of Data Engineering experience
• Strong hands-on experience with GCP, Django, and MongoDB
• Extensive experience with BigQuery
• Advanced SQL skills
• Strong understanding of ETL/ELT and data pipeline development
• Data modeling and data warehousing experience
• Experience handling large-scale datasets and performance optimization
• Strong problem-solving and communication skills

Good to Have

• GCP services such as Cloud Storage, Dataflow, Pub/Sub, Cloud Composer, or Cloud Functions
• Python or other data engineering languages
• Experience with data governance and security
• Agile development experience