Top Data Engineer Jobs in Bengaluru

Maintain and enhance Python-based ETL pipelines and data workflows, build and deploy pipelines for new customers, write clean tested Python code, monitor and troubleshoot production data systems, and optimize data processing performance while collaborating with cross-functional teams.

Design, build, and maintain scalable batch and real-time data pipelines, ETL/ELT workflows, data warehouses and lakes. Ensure data quality, performance, and reliability; integrate data from multiple sources; collaborate with Data Science and ML teams; implement monitoring, validation, and CI/CD; and contribute to the evolution of the data platform and architecture.

Design, build, and maintain scalable batch and real-time data pipelines, ETL/ELT workflows, data warehouses and lakes. Ensure data quality, performance, and cost-efficient processing. Collaborate with Data Science and Engineering to produce ML-ready datasets, implement monitoring/testing, integrate with APIs/cloud platforms, and document data platform architecture.

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.

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.

Senior Azure Data Engineer responsible for designing and implementing end-to-end data pipelines using Azure Databricks, Azure Data Factory, and PySpark. Work with Delta Lake, Spark SQL, and data warehousing concepts to build scalable ETL/ELT solutions; collaborate on analytics and Power BI integration. Availability for 2pm-11pm/12am IST shift required.

Design, develop, and maintain Microsoft Customer Data Platform (CDP) solutions using Azure. Build and optimize data pipelines, integrate multiple data sources (Dynamics 365, Dataverse), create customer profiles and segments, ensure data governance and security, monitor CDP performance, and provide technical support for CDP implementations.

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 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 operate data pipelines and ETL workflows on GCP using BigQuery, Dataflow, Cloud Composer and Dataproc. Develop SQL-based analytics, implement data processing with Python/Java and PySpark, and apply Dataplex for data governance. Collaborate with teams to deliver scalable, production-ready data 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, 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.

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 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.

Design, build, and maintain ETL/data integration pipelines and complex SQL queries. Support reporting and BI tools (PowerBI, Tableau, MicroStrategy). Work with Python and cloud infrastructure; AWS Airflow/Glue and Java/Scala experience preferred. Analyze data requirements, troubleshoot issues, and contribute to data product development. Requires initiative, problem-solving, and relevant engineering degree.

Develop, optimize, and maintain Python/PySpark data applications and ETL pipelines; write SQL; manage data files and orchestration (Airflow); collaborate across teams to design, deploy, and troubleshoot data solutions while following DevOps and cloud (AWS) practices.

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 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.

As a Data Engineer, you'll optimize data workflows, design robust data solutions, and collaborate with teams to support data-driven initiatives.

The Data Engineer will design and maintain data pipelines, build data warehouses, and ensure data quality while collaborating with data scientists and engineers.

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

Build and maintain scalable ETL/ELT pipelines, data warehouses/lakes, and real-time analytics infrastructure. Ensure data quality, governance, and deliver high-quality datasets for product, analytics, and customer-facing dashboards.

Owner of company-wide reporting and analytics for Growth, Marketing, Product, Finance and Operations. Build and maintain dashboards, automated reports and data pipelines (dbt), ensure data quality and governance, run growth and product analyses (CAC, ROAS, LTV, retention), support A/B testing and ad-hoc decision support, leverage AI for automation, and coordinate VAs on data tasks.

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.