Top Data Engineer Jobs in Mumbai

The Quantitative Modeler will join the Portfolio Risk Modeling team to build and maintain risk models, ensuring model governance and collaborating with stakeholders on performance and analytics.

Design, develop, test, deploy, and maintain cloud data platform components and automated pipelines using Airflow, Python, Snowflake and dbt; support teams, ensure performance and scalability, perform root-cause analysis, and provide L2/L3 operational support within Agile SDLC.

Design, build, and maintain cloud-native data pipelines and ETL/ELT workflows for high-volume trade lifecycle data. Integrate DTCC sources (RTTM, Settlement Web) into a Unified Trade Record, process ISO 15022 messages, manage trade exception databases, optimize SQL Server performance, and ensure data quality, lineage, governance, and observability while collaborating with business stakeholders.

Lead design and delivery of enterprise data platform capabilities, building scalable ingestion, transformation and orchestration pipelines using Python, Airflow, Snowflake and dbt. Drive metadata, lineage, data quality, observability, and Agentic AI integrations; support production L2/L3 operations, CI/CD deployments, POCs, cross-functional integration, and mentor engineering teams within an Agile framework.

The role involves designing and building cloud-based data solutions, managing data pipelines, ensuring data quality and performance optimization while leading a team in data engineering.

Build and maintain backend services for an enterprise data catalog using Java and Spring Boot. Design REST APIs, microservices, and event-driven components, integrate relational databases and streaming/messaging, optimize performance and caching, support production stability, and collaborate across product and engineering teams to deliver scalable data integration and governance features.

Embed with partner teams to onboard and validate data products on DPaaS, troubleshoot pipelines, build reusable ingestion/transformation accelerators, run enablement, and contribute AI-assisted onboarding and platform features across structured and unstructured data.

Design, develop, and maintain data solutions and pipelines. Implement ETL processes, ensure data quality, optimize pipeline performance, collaborate with cross-functional teams, and support data migration and cloud storage solutions to enable reliable data operations.

Design, build and maintain scalable batch and real-time data pipelines and data products on AWS to ingest security telemetry, enable analytics/ML, ensure data quality and observability, integrate with enterprise security tooling, and drive DevSecOps/automation for the Group Security Data Platform.

As a Data Engineer, you will innovate and maintain technology services, develop software systems for large projects, and work across all SDLC phases.

Design, develop and maintain PySpark-based ETL pipelines for batch and streaming on Spark/Hadoop. Ensure data quality, optimize Spark performance, build reusable code, validate data pipelines, troubleshoot production issues, and collaborate in agile teams for deployments and documentation.

Lead design and hands-on implementation of scalable cloud microservices and platform solutions. Mentor engineers, drive architecture decisions (DDD, clean/hexagonal), ensure non-functional requirements, document designs, and collaborate across teams to deliver reliable RESTful services used by customers.

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 and implement AI-driven data platforms and pipelines. Build GenAI/LLM workflows and agentic systems, develop production Python/PySpark applications and APIs, optimize Databricks and Snowflake analytics, architect AWS Glue ETL, and apply DBA fundamentals across relational and NoSQL stores.

Design, develop, and maintain data-driven applications for trading operations. Build data pipelines, optimize code, and collaborate with teams for solutions.

Lead architecture and implementation of GitLab's in-product insights and Data Marketplace. Design and coordinate ingestion, modeling, and synchronization into the Knowledge Graph; integrate external systems; publish observability data; define operational readiness; translate ambiguous product problems into technical plans; resolve cross-team dependencies; and mentor other engineers.

The role involves designing and building data systems using various technologies, especially within Microsoft Azure, and requires experience in system integration and data warehousing.

Design, build, and maintain scalable cloud-native data pipelines for trade lifecycle processing. Ingest, transform, and normalize high-volume financial data (DTCC, ISO15022), implement MQ/API-based ingestion, optimize SQL Server performance, maintain UTR schemas and trade exception databases, ensure data quality, lineage and governance, and collaborate with reconciliation, settlements, and operations stakeholders.

Design, build, and optimize high-throughput ETL/ELT pipelines (Glue/PySpark), orchestrate workflows (Airflow/Step Functions), manage and tune multi-terabyte Redshift clusters, lead migrations from Snowflake/RDBMS, integrate heterogeneous sources, implement monitoring and governance, and collaborate with stakeholders to deliver analytics-ready datasets.

Design, build, and maintain scalable ETL/ELT pipelines using BigQuery and GCP. Integrate multiple data sources, optimize BigQuery performance (partitioning/clustering), support real-time and batch processing with Dataflow/Pub/Sub/Airflow, enforce data governance and quality, collaborate with analysts and data scientists, and troubleshoot pipeline reliability and availability.

Looking for a Data Engineer lead to design and build scalable solutions, develop data pipelines, and support analytics and ML initiatives, while collaborating with cross-functional teams for quality data management.

As a Data Engineer Intern, you will write efficient code, learn new technologies, collaborate with teams, and deliver data products to improve decision-making.

As an SAP Data Engineer, you will build extraction pipelines from SAP HANA to AWS S3, ensuring proper data modeling, monitoring incremental loads, and collaborating closely with customers and engineering teams.

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.

Lead strategic development of data quality frameworks and controls within BlackRock's data ecosystem, ensuring reliable and accurate data for decision-making.