Top Data Engineer Jobs in Bangalore

Design, build and maintain ETL mappings and data models; write SQL and Snowflake queries; manage ETL/infrastructure on AWS (EC2); use Informatica, Control-M and ServiceNow; script in Python/Shell/PowerShell; collaborate with onshore/offshore teams and define BI delivery strategy.

Develop and maintain data pipelines and applications using Python and Pandas, apply OOP and data structures, write and optimize SQL, and communicate with stakeholders. Familiarity with Agile, DevOps/CI-CD tools (Jenkins, Git, Jira), AWS, web frameworks (Django/Flask), and Snowflake is a plus.

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.

Designs and maintains scalable data pipelines and infrastructure using Airflow, Databricks, Spark, Python, and SQL. Develops and optimizes ETL/ELT workflows, builds reliable data models, ensures data quality and performance, and supports analytics and business intelligence initiatives. Collaborates with cross-functional teams through Jira and Confluence in an Agile environment while monitoring, troubleshooting, and improving data workflows.

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.

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.

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.

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.

Design and build the data backbone for a no-code data platform: ingestion, schema management, transformation, validation, orchestration, monitoring, observability and AI-assisted features. Implement scalable backend services (APIs, workers, execution engines) using Python/TypeScript/Go, leverage distributed processing (Spark/Flink), ensure data quality, lineage, and SLA monitoring, and collaborate across product, frontend, and QA while mentoring peers.

The Senior Data Engineer role involves data mapping, ETL process development, and collaboration with stakeholders to deliver high-quality data solutions.

Design, build, and optimize enterprise-scale batch and streaming data pipelines using Python, PySpark, Databricks, cloud platforms, dbt, and Airflow. Develop governed, reliable data platforms with strong performance, quality, lineage, security, and cost controls. Responsibilities include data modeling, workflow orchestration, CI/CD automation, Spark optimization, stakeholder collaboration, code reviews, mentoring junior engineers, and driving end-to-end delivery from architecture through production.

The Data Engineer will design and maintain data models, develop BI solutions, integrate data, and optimize data flows while collaborating with stakeholders.

Design, build, and configure Snowflake integration, drive client discussions, manage project deliverables, and integrate data from SAP to Kinaxis.

Design, develop, and support enterprise data platforms, ETL/ELT pipelines, Oracle databases, data warehouses, and risk and regulatory reporting solutions. Build automation, optimize SQL and data processing, ensure data quality, reconciliation, lineage, governance, and audit readiness. Provide L2/L3 production support, resolve incidents, manage batch operations, and perform root-cause analysis. Collaborate with Risk, Finance, Regulatory, Business, and Technology stakeholders while mentoring junior team members.

Designs and maintains ETL/ELT pipelines using Informatica IICS and Snowflake. Develops mappings, taskflows, stored procedures, and data-loading strategies including CDC and SCD patterns. Builds scalable data warehouse solutions, performs data transformation and validation, troubleshoots failures and performance issues, and supports production deployments. Collaborates with business analysts, architects, QA, and technical teams. Preferred experience includes cloud services, storage platforms, CI/CD, scripting, version control, and life sciences or pharmaceutical domains.

Build and enhance enterprise data pipelines into Databricks Unity Catalog and Delta Lake. Improve remediation and entity-matching layers (including D&B DataBlocks), integrate APIs, and support LLM-assisted remediation and CMG redesign projects.

Designs, builds, and maintains ETL/ELT pipelines feeding Microsoft Fabric Lakehouse platforms. Responsibilities include data ingestion, bronze/silver/gold layer development, CDC, incremental loading, data quality, metadata governance, performance tuning, monitoring, automation, and low-level design documentation. The role collaborates with architects, data modelers, and business analysts to deliver reliable, reusable datasets and promote self-service analytics while applying security, resilience, compliance, and cost-optimization standards.

Design, build, and optimize scalable batch and streaming data pipelines, manage Snowflake and BigQuery warehouses, create dbt data models, ensure data governance and observability, collaborate with architecture and security teams, write efficient SQL/Python/PowerShell, and participate in a light on-call rotation to deliver reliable data products.

Design, build, and maintain scalable batch and streaming data pipelines and data models using cloud-native technologies. Improve platform performance, reliability, observability, and automation; perform data quality validation, troubleshoot production issues, participate in architecture and code reviews, and mentor junior engineers.

Design and implement data quality processes, profile and cleanse data, deploy cloud-based data quality solutions (Azure/AWS/GCP), document issues and remediation, report metrics, and collaborate with data teams and stakeholders.

Design, build, and operate scalable batch and real-time data pipelines and analytics platforms. Partner with enterprise customers to define requirements, own technical delivery from discovery to adoption, and present architectures to stakeholders. Drive data governance, observability, CI/CD, and platform optimization while mentoring junior engineers and collaborating with Product, Engineering, and Customer Success teams to enable analytics and AI/ML applications.

Triage and resolve Master Data Management (Party, Risk, Reference) issues in Stibo STEP, run targeted data-cleansing sprints, monitor data quality dashboards, support releases/UAT, and maintain documentation and playbooks to prevent recurring data issues.

The Senior Data Engineer will design and build data infrastructure and pipelines, enhance AI capabilities, and collaborate with teams on data solutions while mentoring junior engineers.