Top Data Engineer Jobs in Pune

Design, build, and maintain scalable data pipelines, warehouses, and lakes; ensure data quality, security, and operational stability; collaborate with data scientists to deploy ML models; lead strategy, governance, and delivery for data modernization and regulatory requirements.

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.

The Engineering Manager leads the Data Engineering team, overseeing the design of scalable data pipelines using AWS technologies, mentoring engineers, and collaborating with stakeholders on data-first initiatives.

Lead design and implementation of AWS cloud data solutions using Python and PySpark. Build ETL pipelines, analytics (Glue, Athena, Redshift), and cloud architectures. Produce HLD/LLD, run cloud readiness assessments, cost estimates, DR planning, and provide technical leadership across onshore-offshore teams while liaising with customers and managed services.

Design, build, and maintain scalable AWS data platforms, implement data governance, and optimize architectures while collaborating with cross-functional teams.

The Threat Intelligence Data Engineer designs and operates automated data collection systems for cybersecurity, focusing on discovering and indexing data from various web sources, including dark web and decentralized networks.

Design, build, and maintain data pipelines, warehouses and lakes; implement ETL and processing algorithms; ensure security, monitoring and CI/CD; collaborate with data scientists and lead or coach team members to deliver data solutions.

Design, build and maintain secure, scalable AWS data pipelines, data lakes and Snowflake-based warehouses. Develop ETL/ELT (DBT, PySpark), orchestration (Airflow), advanced SQL/PLSQL, and collaborate with data science to deploy ML models while leading teams and ensuring governance, controls and stakeholder alignment.

Designs, builds and maintains data pipelines, data warehouses and data lakes; implements processing and analysis algorithms; ensures data accuracy, security and accessibility; collaborates with data scientists to deploy ML models; may lead and supervise a team and enforce risk, controls and governance.

Design, implement and support analytical data infrastructure and real-time pipelines on AWS. Extract, transform and load data from varied sources using SQL and big-data tools; enable analytics, monitoring, and machine-learning workflows; improve reporting and automate self-service data processes.

Build and maintain data pipelines, warehouses and lakes; design architectures for secure, scalable data processing; develop processing algorithms; collaborate with data scientists to deploy ML models; lead or support team members and embed risk, controls and governance.

The Senior Azure Data Engineer will design, develop, and maintain data infrastructure and pipelines, ensuring data quality, integrity, and alignment with business objectives. Responsibilities include collaborating with teams and establishing best practices for data engineering.

Build and enhance enterprise data pipelines into Databricks Unity Catalog and Delta Lake. Improve remediation and entity-matching layers (including D&B DataBlocks), integrate APIs, and support LLM-assisted remediation and CMG redesign projects.

The Data Engineer role involves designing and maintaining ETL pipelines, optimizing data storage solutions, collaborating with teams, and ensuring data accuracy and integrity.

Design, build, and maintain centralized data pipelines, warehouses, lakes and hubs. Assemble complex datasets, implement ETL processes, build analytics tools and reports, support cross-functional teams, ensure secure cross-region data separation, and create product data tools to enable insights on acquisition and operational metrics.

Design, build, and operate scalable ETL/ELT pipelines using open-source tools. Optimize databases and SQL, prototype new tools, support migration from Azure Databricks to AWS, ensure data quality, observability, CI/CD, and collaborate across distributed teams in Advertising and Market Research.

Design, build, and maintain Azure-based data infrastructure and pipelines. Implement data models and schemas, ensure data quality, optimize distributed storage and processing (Databricks/PySpark), and establish engineering best practices while partnering with cross-functional teams.

Build and maintain scalable batch and real-time data pipelines using Kafka, Maxwell, Spark and dbt; design medallion-based data models; implement cloud-native data platforms (S3, Trino, BigQuery); optimize performance, ensure data quality, observability, and SLA adherence; enable analytics through trusted datasets and Metabase dashboards.

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.

Lead design, develop, test, and maintain cloud-native risk platform components on AWS. Build containerized systems (EKS/Docker/Helm), backend services in Python using Boto3, implement IaC and CI/CD, collaborate with architects and stakeholders, and support risk analytics, data modeling, reporting, and production troubleshooting.

Lead a two-month project to assess the current data environment and design, build, and optimize a scalable cloud data warehouse. Deliver automated ETL/ELT pipelines, dimensional models, analytics-ready datasets, documentation, and handover while partnering with analysts and stakeholders for BI and reporting.

Design, build, and operationalize AI-enabled data pipelines and production-grade AI applications using Python and Snowflake. Prepare and transform large datasets, develop features and embeddings, support deployment and monitoring of ML/LLM models, optimize Snowflake for AI workloads, and collaborate with data science and engineering teams to deliver scalable APIs and RAG-enabled data workflows.

We are looking for a Data Engineer with skills in Python, SQL, and Azure technologies to develop Big Data applications. Responsibilities include working with Spark and Docker to handle large scale data processing and analytics.

The Azure Data Engineer will build data pipelines and dataflows, implement security modules, and work with Azure services while supporting business requirements and data analysis.

The Data Engineer role involves analyzing raw data, building data systems, conducting complex analyses, and enhancing data quality by collaborating with data scientists and architects.