Top Data Engineer Jobs in Mumbai

Design, build, and maintain secure, scalable data collection, storage, pipeline, and analytics solutions on AWS. Develop production-quality Java/Python code, implement data serialization, streaming, and orchestration, and ensure data governance, backup, and security practices.

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.

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.

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, develop, and maintain data solutions and ETL pipelines on AWS BigData. Ensure data quality, monitor and optimize data workflows, troubleshoot pipelines, document processes, and mentor junior team members while collaborating with cross-functional teams.

Design, develop, and maintain cloud-based data solutions and ETL pipelines. Ensure data quality, optimize workflows, implement CI/CD for data operations, document processes, collaborate with stakeholders, and mentor junior engineers.

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.

Build and maintain scalable batch and real-time data pipelines on GCP to support analytics and AI. Develop reusable data products, implement data quality, schema management, and metadata enrichment. Collaborate with architects, analysts, and ML teams to enable AI-ready data platforms, monitor/operate pipelines, and follow engineering best practices.

Design, build, and operate scalable batch and real-time data pipelines on GCP. Develop reusable data products, semantic models, and AI-ready datasets. Implement ETL/ELT, streaming, orchestration, monitoring, and governance. Collaborate with architects, analytics, and AI teams, support data modernization and production stability, and follow DevOps and engineering best practices.

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 and hands-on build cloud-native, microservice-based systems for the PitchBook platform. Drive architecture and design, implement testable scalable code, define non-functional requirements, mentor engineers, collaborate with architects and stakeholders, and deliver end-to-end initiatives while promoting engineering best practices and continual improvement.

Lead architecture and engineering for large-scale, cloud-native data and application platforms on AWS. Design relational and NoSQL data solutions, streaming/ingestion and lakehouse patterns, integrate AI/ML/GenAI (LLMs, RAG, embeddings, vector DBs) into production, establish enterprise standards, SLOs/SLAs, observability, security, and cost controls, and grow senior technical talent and architecture governance across the organization.

Build and manage data connections, validate and normalize incoming portfolio files, automate monitoring and reporting, improve auto-reconciliation via data mining and transformations, support operations and development requests, and communicate technical solutions to non-technical stakeholders. Participate in light on-call duties.

Design, build, and maintain data connections and transformations, validate file deliveries, normalize external datasets, automate monitoring/reporting, improve auto-reconciliation, support operations and development, and communicate technical details to stakeholders.

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.

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.

Design and develop data-driven solutions and scalable data pipelines using Python and SQL. Leverage Azure data products to build robust, secure architectures, ensuring data integrity, performance, and governance while supporting data-centric client projects.

Design and develop scalable data-driven solutions and pipelines using Python and SQL. Leverage Azure data services to build secure, high-performance architectures, ensuring data integrity, governance, and overall success of data projects.

Design and build scalable data pipelines and architectures on Azure using ADF, Databricks, and Synapse. Implement Kimball/Data Vault modeling, write ETL in Python and T-SQL, ensure data governance (Azure Purview), automate tests, troubleshoot issues, document processes, and collaborate across teams.

As a Data Engineer, you will work on data systems, participate in cloud migration, analyze data for product performance, and maintain data infrastructure.

Design, build, and maintain scalable backend and full-stack systems; integrate AI capabilities (RAG, LLMs, vector search) into applications; collaborate across teams, participate in architecture and system design, resolve performance and reliability issues, write testable code, and improve development processes and tooling.

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.