Top Data Engineer Jobs in Mumbai

The role involves designing scalable data pipelines and architectures using Microsoft Fabric, client engagement for requirements gathering, and optimizing data solutions for analytics and reporting.

As a Data Engineer, you will design, develop, and maintain data integration solutions, optimize ETL/ELT pipelines, and support data workflows for analytics and reporting.

As an AI Data Engineer, you'll deploy and optimize AI models on Azure, manage upgrades, implement MLOps pipelines, and ensure compliance.

Design, build, and maintain scalable ETL/ELT and real-time data pipelines on AWS. Manage Airflow DAGs, Kafka ingestion, Redshift optimization, data quality checks, API integrations, and cost optimization while collaborating with BI for reporting needs.

Deploy, monitor, and optimize enterprise AI models on Azure. Implement MLOps pipelines (CI/CD) with Azure ML and GitHub Actions, ensure governance, telemetry, drift detection, cost/performance tuning, versioned rollouts, and compliant change management across client-facing applications.

Support data ingestion and pipeline reliability for marketing data feeds; run and validate ML models for trend detection; test tokenisation/anonymisation for regional compliance; maintain dashboards and front-end interfaces; manage AI briefing intake and map historical campaign data; automate CSV macros and tune generative AI prompts for paid media workflows.

Build and maintain real-time data pipelines and front-end dashboards; train, deploy, and monitor ML models for trend detection and release success scoring; implement data tokenisation/anonymisation for cross-border compliance; automate briefing intake and ad deployment workflows using LLMs and Gen AI; collaborate with paid media and creative teams to operationalise AI-driven marketing solutions.

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.

Design, build, and maintain scalable ETL/data pipelines and data management blueprints. Ingest, transform, and curate large complex datasets for analytics, support cloud (AWS) implementations, enable downstream consumers, and provide technical leadership and operational support to ensure data governance, quality, and automation.

Develop and maintain data pipelines using AWS technologies. Collaborate with teams to design data solutions and optimize existing systems. Create data models and perform end-user querying.

Design, build, and optimize scalable graph data pipelines and models using Python and SQL. Develop ingestion, ETL, validation, and query frameworks for graph databases, optimize performance and indexing, ensure data quality, collaborate with cross-functional teams, automate workflows, troubleshoot production issues, and document schemas and best practices.

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.

As a hands-on data engineer, you'll build and enhance scalable data platforms, develop data pipelines, and collaborate with various teams to improve data solutions and system performance.

Design, assess and document end-to-end data flows and lineage; identify critical data elements and data quality rules; perform data risk and quality assessments; build and optimize ETL/ELT pipelines (CI/CD); engage stakeholders, prioritize with product owners, and improve data management and literacy across teams.

The AWS Data Engineer will develop and maintain data pipelines using AWS technologies, perform data modeling, and collaborate with teams to meet data needs.

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.

The role involves leading data architecture and engineering for a health insurance platform, focusing on cloud-native solutions, scalable data pipelines, and ensuring data quality and governance while collaborating across teams.

Lead technical direction for data platforms, design and maintain data pipelines for reporting and AI (RAG, embeddings, vector stores), reduce architectural debt, mentor engineers, partner with product leadership, and represent Mitratech externally.

Design, build, and maintain scalable ETL pipelines and data models for analytics and ML. Ensure data quality, optimize performance, enforce governance and security, and collaborate with stakeholders to deliver reliable, production-ready data for reporting and data science.

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 cloud-based ETL/data pipelines in Snowflake. Extract and transform data from heterogeneous sources, optimize SQL performance, implement monitoring, and collaborate across teams. AWS and Python experience are advantageous.

Design, build and maintain ETL mappings and data models; write SQL and Snowflake queries; manage ETL/infrastructure on AWS (EC2); use Informatica, Control-M and ServiceNow; script in Python/Shell/PowerShell; collaborate with onshore/offshore teams and define BI delivery strategy.

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.

Design, build, and maintain cloud data pipelines and streaming solutions using Java, Kafka, AWS services, SQL/warehouse technologies, and Python. Implement CI/CD, automated testing, and resilient microservice-based systems. Collaborate in a small agile team and ensure secure identity/authentication controls.

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.