Top Data Engineer Jobs in Bengaluru

Design, build, and maintain scalable ETL/ELT pipelines and data warehouses using Snowflake, AWS Glue, Python, and SQL. Optimize performance, implement CI/CD with Azure DevOps, ensure data quality and governance, collaborate with analytics teams, and troubleshoot pipeline issues.

Looking for a Data Engineer to build scalable data pipelines with PySpark, perform impact analysis, and ensure data quality in collaboration with the Group Operations Team.

The Data Engineer will build scalable data pipelines, perform data analysis and ensure high-quality data solutions aligned with enterprise data models.

The Data Engineer collaborates with Group Risk to create and support data pipelines for IFRS9 reporting, utilizing PySpark and Informatica BDM, ensuring data quality, and overseeing ETL processes.

The AWS Data Engineer will design and implement data pipelines using AWS services, develop ETL processes, and optimize data storage solutions.

Design, implement, and maintain data pipelines using Elasticsearch and data warehousing solutions, integrate data sources, and ensure data accuracy while collaborating with cross-functional teams.

Design, build, test, and deploy ETL/ELT data pipelines and data‑warehouse solutions for AML/compliance domains. Produce technical specifications, coordinate stakeholders, automate data ingestion controls/tests, implement DevOps practices, and support releases while ensuring data quality, lineage, and regulatory requirements are met.

Design, build, and maintain scalable Azure-based data pipelines, lakes, and warehouses (ADF, Databricks, ADLS, Synapse). Implement ETL/ELT for batch and real-time ingestion, ensure data quality, security, and governance, optimize performance and cost, support production pipelines, enable analytics, and implement CI/CD and documentation.

The Data Engineer will design and develop scalable data architectures and real-time streaming pipelines, manage enterprise data ecosystems, and ensure data governance and quality, primarily within the banking sector.

Design, build, and maintain scalable batch and streaming data pipelines and ETL processes. Develop Python/PySpark code, manage databases/data warehouses (Snowflake, PostgreSQL, Aurora), ensure data quality and security, collaborate with analysts, engineers, and stakeholders, and integrate data solutions into the platform architecture.

Design, build, and optimize scalable PySpark-based ETL pipelines using the Apache ecosystem. Ensure production reliability, performance tuning, data quality, lineage, and integration of structured/unstructured sources. Collaborate with data scientists, analysts, and stakeholders; write maintainable Python code and support CI/CD and version control workflows.

Design, build, and maintain scalable AWS-based data pipelines using Python, Spark, and SQL. Manage data in S3, catalog with Glue Crawlers, query with Athena, and process with EMR. Implement serverless workflows with Lambda, monitor with CloudWatch, integrate alerts via SNS, and support dashboards (QuickSight/Power BI/Tableau) for analytics and reporting.

Design, build, and maintain scalable Azure-based data pipelines and storage solutions (Data Factory, Databricks, Data Lake). Develop ETL/ELT processes, optimize pipeline performance and cost, ensure data security/compliance, collaborate with stakeholders, and document data architectures. Support BI/reporting via Power BI and apply DevOps/CI-CD practices.

Design, build, and maintain scalable AWS-based ETL pipelines and data warehouse solutions. Process large datasets with Python and PySpark, use S3 and Glue for storage and processing, write optimized SQL, maintain PostgreSQL data models, ensure data quality and performance, and collaborate with stakeholders to troubleshoot pipeline issues.

Design, build, and maintain scalable Azure-based data pipelines and storage solutions using ADF, Databricks, Synapse, Data Lake, and Cosmos DB. Develop ETL/ELT processes, optimize performance and cost, ensure security and compliance, collaborate with stakeholders, troubleshoot pipelines, and document data workflows. Support BI/visualization (Power BI) and CI/CD/DevOps practices.

Design, develop, and maintain scalable Scala-based data processing applications and ETL pipelines. Build distributed systems using Spark, Akka, and streaming tools (Kafka/Akka Streams). Optimize performance, ensure code quality, collaborate with product/QA/DevOps, and produce technical documentation. Work with SQL and NoSQL databases and support CI/CD and cloud deployments.

Design, build, and maintain scalable ETL pipelines using Python and PySpark on AWS. Process large datasets in S3 via Glue, EMR, or Lambda; manage orchestration with Step Functions, Glue Workflows, or Airflow. Ensure data quality, optimize performance, collaborate with data consumers, and participate in data modeling and documentation.

Design, build, and maintain scalable data pipelines and ETL/ELT workflows using Python, SQL, and Spark on Azure. Manage data storage (Data Lake, Blob, Synapse), ensure data integrity, governance, and security, monitor pipelines, troubleshoot performance issues, and collaborate with cross-functional teams. Follow best practices, participate in code reviews, and use CI/CD and version control.

Design, develop, and optimize AWS Glue ETL pipelines integrating data from S3, RDS, Redshift, and other AWS services. Transform data into analytics-ready formats, ensure data quality and security, tune performance, orchestrate workflows (Step Functions/Airflow), monitor jobs, and collaborate with analysts and stakeholders to deliver scalable data lake and warehouse solutions.

Design, build, and maintain large-scale data pipelines and ETL/ELT workflows using Spark and Scala. Implement data models and warehousing on Snowflake and cloud (AWS/GCP). Ensure data quality, governance, security, and performance optimization while collaborating with data scientists, analysts, DevOps, and stakeholders.

Build and maintain scalable data pipelines and ETL/ELT processes using SQL and BigQuery; support ML model deployment and monitoring; ensure data quality, governance, and analytics readiness; collaborate with data scientists and stakeholders in an agile environment.

Design, build, and maintain ETL data pipelines using Talend and Snowflake. Extract data from APIs, reverse-engineer Alteryx workflows, apply Data Vault modeling, implement CI/CD and AWS data services, document designs, and collaborate with architects, SMEs, and developers to deliver robust cloud-based data warehousing solutions.

Design and deliver scalable, highly available data architectures and pipelines (batch and streaming). Lead technical delivery, mentor teams, manage client engagements, ensure security/compliance, and drive implementation using cloud (AWS/Azure/GCP), ETL, data lake/warehouse, and best SDLC/Agile practices.

Design, build, and maintain ETL/data pipelines using SQL and Python with Airflow orchestration. Implement data quality checks, optimize pipelines for performance, document workflows, and collaborate with analysts/scientists to deliver scalable data solutions.

Senior Data Engineer responsible for designing and building scalable, fault-tolerant data pipelines and platforms. Work with clients to recommend and implement solutions using cloud data services (batch and streaming), manage ingestion, storage, processing, automation, quality control, migration and deployment. Lead customer implementations, run demos, manage risks, and bring best-practice insights on Databricks, Snowflake, ETL tools, and cloud data architectures.