Top Data Engineer Jobs in Bengaluru

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.

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.

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 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, 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 data pipelines, ETL processes, and data models to support analytics and product teams. Ensure data quality and reliability, instrument data workflows, and collaborate with US and global engineering teams to operationalize data infrastructure for SPAN's electrification and home energy solutions.

Design, build, and maintain scalable data pipelines and infrastructure while optimizing data systems. Collaborate with teams to ensure data reliability and compliance, and document internal standards.

Build and scale end-to-end ETL/ELT pipelines (batch and near-real-time), design ingestion systems, optimize cloud data warehouses, enforce data quality and monitoring, define data models/contracts, enable ML/AI workflows, and operate data infrastructure using Python, PostgreSQL, MongoDB, and AWS.

Develop ETL pipelines and data transformations for analytical reports. Responsibilities include data ingestion, transformation, troubleshooting, and ensuring data structures for reporting support.

Clean, reconcile, and transform messy client data into validated, production-ready datasets. Build and maintain data transformation and validation pipelines, diagnose discrepancies, document mappings, and collaborate with engineering and client teams to deliver high-quality data for ML models.

Design, build, and optimize enterprise-scale data pipelines and real-time streaming solutions using Databricks, Spark, Kafka, Delta Lake, Python, and cloud platforms (Azure/AWS). Implement data architectures, integrate SQL/NoSQL systems, apply governance and performance best practices, and operate containerized microservices on Kubernetes.

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 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.

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.

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, 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, 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 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, 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.