Top Data Engineer Jobs in Bengaluru

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.

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.

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 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.

Build, optimize, and maintain scalable AWS-based data pipelines and data lakes (Snowflake/Databricks). Implement streaming ingestion (Kinesis/Kafka), CDC, automated data quality checks, and ML-ready feature stores. Collaborate with Product, ML, and Analytics teams, own pipeline QA, observability, and participate in on-call rotations to resolve production data issues.

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.

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.

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 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, 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, 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 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.

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.

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 will develop and manage data pipelines, ensuring reliable data ingestion and delivery while optimizing for simplicity and performance.

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.

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

Design, build, and maintain production data pipelines and integrations across applications, databases, and cloud services. Support data architecture, ingestion, embedding workflows for AI, data quality, monitoring, and platform reliability. Collaborate with Full Stack and AI teams to deploy scalable cloud-based data infrastructure and establish data engineering standards and best practices.

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