Top Data Engineer Jobs in Bangalore

Design, build, and maintain scalable data pipelines and ETL processes using Python and PySpark. Orchestrate workflows with Airflow, query data with Trino and Hive, and write efficient SQL. Collaborate in Agile/Scrum teams to deliver data solutions and optimize data platform performance.

Design, build, and maintain ELT/data pipelines and workflow orchestration using Airflow, Python, and PySpark. Develop custom connectors to ingest diverse data, implement DataOps and data CI/CD pipelines, monitor and troubleshoot pipelines, validate data quality, collaborate with cross-functional teams, document workflows, and work with large-scale distributed datasets and visualization tools.

Deliver data engineering and analysis work using Python, PySpark, SQL and data platforms (Airflow, Trino, Hive). Produce LLD, coordinate offshore teams, follow Agile/Scrum, and work with container platforms (Linux, OpenShift, Kubernetes) and visualization (Superset). Banking domain experience is a plus.

Design, develop, deploy, and administer SSIS-based ETL solutions and data pipelines. Optimize SSIS packages and SQL performance, support data warehouse modeling, monitor ETL operations, provide L2/L3 production support, enforce data quality and governance, and collaborate with BI teams for reporting and migrations across environments.

Lead data engineering projects, optimize large-scale data pipelines and platforms, ensure delivery quality, manage risks, and collaborate with analytics and business leaders to align technical work with business goals.

Design, build, and optimize ETL pipelines and data warehouses using SQL, AWS Glue, and S3. Investigate and resolve data issues, develop Python-based solutions and small web apps, and own requirements, design, and end-to-end delivery for change requests.

Design, build, and maintain scalable AWS-based ETL/ELT data pipelines and data platform solutions. Develop SQL and Python code, create tests and documentation, monitor and optimize pipeline performance, and collaborate with BI, AI/ML, and infrastructure teams while following best practices and data governance.

Design, build, and maintain scalable AWS-based data platform services and high-performance data pipelines. Provide technical leadership, establish data engineering best practices, and partner with cross-functional teams to deliver data integration, transformation, governance, and physical data models for healthcare solutions.

Build and operate a scalable data platform: design and maintain ETL/ELT pipelines, Bronze/Silver/Gold data layers, data models and semantic layers, manage migrations and schema changes, ensure data quality/observability, enable analytics and future event-driven/recommendation systems, and use AI-assisted workflows to accelerate engineering.

Design, build, and deliver production-ready Data & AI solutions on Microsoft Azure. Develop and maintain data pipelines and lakehouse architectures using Fabric, Databricks, and Azure services; implement CI/CD, optimize production workloads, and collaborate with architects, project teams, and clients.

Lead the data team to build scalable data products, create data pipelines, partner with business teams, and ensure data quality while impacting revenue and decision-making.

The role involves defining and architecting AI-ready data platforms, designing ETL pipelines, and establishing data architecture standards for analytics in the Life Sciences and Healthcare sectors.

Designs and refines data platform blueprints, collaborating with integration and data architects to align systems, data models, and platform components. Supports data integration, transformation, automation, optimization, and architecture improvements in large-scale cloud data environments. Works independently as a subject matter expert, contributes solutions, participates in team discussions, and guides junior team members. Requires Databricks expertise and strong understanding of data modeling and cloud data platforms.

Designs and refines data platform blueprints, collaborates with integration and data architects, and leads implementation of scalable, interoperable platform strategies. The role oversees platform optimization, supports data visualization solutions, translates complex datasets into actionable insights, coordinates decisions across teams, and promotes knowledge sharing and continuous improvement.

Contributes to the design and refinement of data platform blueprints, collaborating with integration and data architects to align systems, data models, and platform components. Develops data integration and transformation solutions in large-scale environments, supports cloud-based platform operations, identifies architecture improvements, and automates workflows. Works independently as a subject matter expert, contributes to team discussions, solves technical problems, and guides junior team members.

Contributes to the design and refinement of data platform blueprints, collaborating with integration and data architects to align platform components, systems, and data models. Supports data integration, transformation, workflow automation, platform optimization, and architecture strategy. Works independently toward subject-matter expertise, contributes to team solutions, and mentors junior team members.

Designs and refines data platform blueprints, collaborates with integration and data architects, and supports cohesive system and data model integration. The engineer identifies platform improvements, contributes solutions, participates in technical discussions, aligns components with business needs, and guides junior team members. The role requires Databricks expertise, data integration and transformation experience, cloud data platform knowledge, data modeling, and scripting or automation skills.

Contributes to the design and refinement of data platform blueprints and components. Collaborates with integration and data architects to ensure cohesive system integration and consistent data models. Develops solutions, identifies platform optimizations, supports data integration and transformation, and applies cloud platform, scripting, and automation knowledge. Works independently as a subject matter expert, participates in team discussions, and guides junior team members.

Develops and refines data platform blueprints and designs, integrating platform components across systems and data models. Collaborates with architects and cross-functional teams, identifies platform improvements, supports data integration and transformation, and provides technical guidance to junior team members. The role requires Databricks expertise, cloud data platform knowledge, data modeling, and scripting or automation skills.

Designs and refines data platform blueprints, collaborates with integration and data architects, and supports cohesive system and data model integration. The role contributes to data platform solutions, identifies architecture improvements, supports automation and data workflows, and aligns platform components with business needs. It requires Databricks expertise, data integration and transformation experience, cloud data platform knowledge, data modeling skills, and the ability to work independently as a subject matter expert while mentoring junior team members.

Lead design and delivery of scalable batch and real-time data platforms, build and maintain reliable pipelines and data models, ensure data quality and governance, integrate third-party data, collaborate with cross-functional teams, and mentor engineers.

Lead the design, development, and maintenance of data infrastructure, pipelines, ETL processes, and data platforms using Snowflake, Python, SQL, Databricks, and cloud services. Optimize systems for scalability, implement data quality and governance standards, support structured and unstructured data, collaborate with stakeholders, conduct code reviews, document solutions, and contribute to architecture and knowledge-sharing initiatives.

Design, build, and manage scalable, secure petabyte-scale data pipelines on AWS. Collaborate with Data Scientists and Engineers to optimize processing, implement integration tests, review code/design, plan capacity, document deployments, and align engineering efforts with product and business goals.

Lead design and delivery of enterprise data warehouse solutions: architect and optimize ETL/ELT pipelines (SAP BODS, DataStage), build scalable data models, tune SQL/Oracle performance, enforce data governance/security, mentor engineers, collaborate with BI and stakeholders, and support L3 production operations with rotational shift coverage.

The Senior Data Engineer will manage data ingestion pipelines, ensuring reliability and efficiency while collaborating with multiple teams. Responsibilities include developing scalable data solutions and troubleshooting complex issues.