Top Data Engineer Jobs in Bangalore

Design, build, and maintain ELT/data pipelines and workflow orchestration using Airflow, Python, and PySpark. Develop custom connectors to ingest diverse data, implement DataOps and data CI/CD pipelines, monitor and troubleshoot pipelines, validate data quality, collaborate with cross-functional teams, document workflows, and work with large-scale distributed datasets and visualization tools.

Design, develop, and optimize PL/SQL artifacts and Python ETL scripts; tune SQL and Oracle performance; ensure data integrity and availability; integrate systems via APIs; support production deployments and monitoring; collaborate with engineers and analysts; administer Oracle and PostgreSQL; use GitLab and Jira for version control and tracking.

Develop and maintain data pipelines and applications using Python and Pandas, apply OOP and data structures, write and optimize SQL, and communicate with stakeholders. Familiarity with Agile, DevOps/CI-CD tools (Jenkins, Git, Jira), AWS, web frameworks (Django/Flask), and Snowflake is a plus.

Design, develop, and optimize ELT/ETL pipelines and data models using SQL, Python, and cloud services (AWS). Enable cloud data movement to Snowflake, perform SQL performance tuning, support code migrations, and collaborate in agile, distributed teams to deliver analytics-ready data solutions.

Lead database design, data modeling, migrations, and schema evolution across MySQL/Postgres and GCP databases. Design and operate config-driven ETL/ELT pipelines and streaming ingestion, enable CI/CD, troubleshoot data flows, and mentor offshore DB/ETL engineers.

Design, build, and maintain ELT/data pipelines and workflow orchestration using Airflow, Python, and PySpark. Develop custom connectors, implement DataOps and CI/CD for data, monitor and validate data flows, collaborate with cross-functional teams, and document data workflows and governance.

Design, build, and maintain scalable data pipelines and ETL processes using Python and PySpark. Orchestrate workflows with Airflow, query data with Trino and Hive, and write efficient SQL. Collaborate in Agile/Scrum teams to deliver data solutions and optimize data platform performance.

Design, develop, and coordinate cloud data solutions using Snowflake, Python, and AWS. Support product releases, deployments, testing, and post-install support. Collaborate with architects and tech leads, troubleshoot issues, and maintain documentation. Contribute to agile squads, adopt new technologies, and enable Fidelity Wealth business data needs.

Perform data migration/refresh from production, support Python-based data refresh framework, run post-migration fixes, validate and reconcile data, monitor and resolve batch jobs and pipeline issues, perform performance checks, and coordinate with dependent teams.

Join the Event Streaming and Data Replication platform as a Data Engineer to design, build, and operate cloud-native data platforms. Work with Airflow, Spark, Kubernetes, and Python; deploy containerized apps on AWS/Azure; use IaC (Terraform/CFT/ARM); implement CI/CD (GitHub, Artifactory, Jenkins); configure monitoring and alerts; and collaborate across teams to deliver highly available, scalable solutions.

Design, build, and maintain cloud data pipelines and architectures using SQL, Python, Snowflake, and AWS. Implement CI/CD for data workflows, use developer assistance tools, and collaborate across teams to deliver reliable data solutions.

Design, build, and maintain scalable ELT/data pipelines and connectors using Airflow, Python, and PySpark. Implement DataOps and CI/CD for data workflows, monitor and troubleshoot pipelines, validate data quality, collaborate with cross-functional teams, and document data processes and governance.

The Lead Data Engineer will oversee enterprise data engineering services, focusing on data pipeline management, technical strategy, governance, and operational excellence across various data platforms and tools.

Develop, test, and maintain enterprise applications and services; support API development and system integrations; collaborate with business and technical teams in Agile; troubleshoot defects and performance issues; maintain documentation and support deployments.

Develop, test, and maintain enterprise applications and services; build APIs and system integrations; collaborate with business and technical teams in Agile; troubleshoot defects and performance issues; maintain documentation and support deployments.

Design and build scalable data pipelines, ETL, streaming ingestion and transformations. Collaborate with stakeholders to model data, automate quality testing, reduce platform costs, and embed new data techniques across the organisation while ensuring security and operational controls.

Lead design and implementation of scalable data architectures and pipelines, support deployed data applications and models, ensure data governance, ingest streaming/batch/API data, optimize ML libraries, mentor junior engineers, collaborate cross-functionally, and drive data product development and operational excellence.

The Data Engineer P2 designs, develops, and maintains cloud-based data infrastructure, implements data pipelines, and collaborates with cross-functional teams to fulfill data requirements.

Design, develop, deploy, and administer enterprise ETL solutions using SSIS. Build and optimize ETL pipelines, write advanced T-SQL, support data warehouse design and migrations, monitor and tune performance, ensure data quality/governance, provide L2/L3 support, and document processes while collaborating with BI teams.

Lead the data team to build scalable data products, create data pipelines, partner with business teams, and ensure data quality while impacting revenue and decision-making.

The role involves defining and architecting AI-ready data platforms, designing ETL pipelines, and establishing data architecture standards for analytics in the Life Sciences and Healthcare sectors.

The Data Quality Engineer ensures data reliability and integrity by designing systems for data validation, monitoring data pipelines, enforcing quality standards, and collaborating with teams for data-driven decisions.

The Senior AI Data Engineer architects and manages data solutions, ensuring high quality data for AI products, leading technical projects, and mentoring team members in data engineering practices.

Design, develop, and implement large-scale Azure-based data engineering solutions. Build and maintain data lakes, ETL/streaming pipelines using ADF, EventHub, Databricks, Functions, and ASA. Use SQL, Python, Spark, and work with Teradata, Cassandra, MongoDB, SQL Server, Snowflake and ADLS. Implement CI/CD with GitHub, Azure DevOps and Terraform. Collaborate in an Agile/DevOps hybrid model.

Design, develop, and implement large-scale Azure-based data engineering solutions using ADF, Databricks, EventHub, Functions, Data Lake and SQL. Build ETL/streaming pipelines, perform data analytics with SQL, script in Python/Spark/Unix, support/adopt CI/CD (GitHub/Azure DevOps/Terraform), and work with platforms like Teradata, Cassandra, MongoDB and Snowflake in an agile DevOps environment.