Top Data Engineer Jobs in Bangalore

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.

The Data Architect will design and implement data infrastructure, collaborating with teams to enhance data usage for analytics, automation, and AI, while ensuring compliance and governance.

Develop, test, and implement data engineering solutions. Analyze datasets, build ETL jobs, and manage data structures for analytics.

Lead a team of Data Engineers to modernize cloud infrastructure, migrate data to Google BigQuery, and develop scalable ETL pipelines.

The Data Operations Analyst ensures smooth implementation and delivery of data to portfolio managers by supporting data pipelines, validation, cleaning, and documentation. Requires technical expertise in support roles and specific tools.

Design, develop, and maintain ETL/data pipelines using SSIS; manage and optimize MySQL databases and SQL queries; build and deploy SSRS reports; lead data warehousing and integration efforts; troubleshoot data engineering issues and collaborate with stakeholders.

Design, build and operate production ETL/ELT data pipelines in Python and FastAPI, maintain Spark/Scala workloads, ensure data quality, lineage and observability, collaborate with product and AI teams to deliver AI-ready data products, and apply testing, CI/CD and schema management.

Design, build and maintain cloud-oriented big data solutions for ingestion, processing, cleaning and exposition. Improve data quality, implement ETL and data-warehouse architectures, and collaborate with DDS/Data Foundation teams to ensure platform reliability.

Design, implement, and optimize Apache DolphinScheduler workflows and ETL pipelines. Integrate with Hadoop, Spark, Flink, Hive, and Kafka. Develop custom plugins, troubleshoot performance and scalability issues, monitor job execution and resource allocation, and maintain CI/CD, version control, and infrastructure automation while collaborating with data engineers and DevOps teams.

Design, build, and optimize PySpark applications and ETL pipelines to process large-scale datasets from relational, NoSQL, file, and streaming sources. Ensure data quality, implement error handling, and collaborate with analysts, scientists, and architects to deliver performant data solutions.

Design, develop, test, and deploy high-performance Spark/Scala data processing applications and ETL pipelines on Cloudera (CDH). Optimize Spark and Cloudera platform performance, integrate HDFS/Hive/Impala/HBase/Kafka, ensure data integrity/security, and implement version control and CI/CD for production pipelines.

Design, develop, and maintain integration solutions ensuring data accuracy and security, while leading junior engineers and optimizing processes.

Design, build, and scale end-to-end data products on a cloud lakehouse (Databricks/Snowflake) using Python, Spark, dbt and SQL. Implement medallion architecture, data models, governance (Unity Catalog), observability, and CI/CD. Lead technical design, mentoring, monitoring, and stakeholder collaboration across Supply Chain, Manufacturing, and Quality domains.

As a Senior Data Engineer, you will design and manage real-time and batch data pipelines, collaborating with various teams to enhance data analytics capabilities.

Build and maintain end-to-end data pipelines using PySpark and SQL on Databricks/Delta. Establish data modeling and pipeline best practices, create AI-ready analytical datasets, troubleshoot pipeline issues, and collaborate with stakeholders to embed business logic and optimize reliability, efficiency, and performance.

Design, build, and optimize high-volume data pipelines using Python, Databricks and Spark on Azure. Create and maintain automation test suites (Azure DevOps), perform manual/load/exploratory testing, collaborate with BAs and developers to meet sprint goals, and assist with sprint estimation and delivery.

Design, build, and operate scalable data engineering services and ingestion pipelines (CDC/streaming). Develop and optimize a centralized analytics warehouse, data models, and aggregation strategies. Build APIs, enforce multi-tenant security and data governance, monitor pipeline health, improve automation and observability, and ensure production reliability through strong engineering practices.