Top Data Engineer Jobs in Mumbai

Design, build, and optimize Snowflake data models and dbt pipelines. Create Power BI reports and paginated RDLs with DAX and row-level security. Manage CI/CD with Azure DevOps, version control with Git, enforce data quality and governance, and optimize warehouse cost and performance while collaborating with business stakeholders.

Data Engineer responsible for building and maintaining ETL pipelines, data processing workflows, and data platforms. Must have strong hands-on experience with Healthcare Revenue Cycle Management and understanding of healthcare data workflows. Role requires hybrid (onsite + offshore) work and immediate availability.

The Data Engineer will build and manage scalable data pipelines and databases, collaborate on architecture, and drive data systems towards real-time capabilities in a fast-paced, innovative environment.

The Data Science Engineer will analyze and prepare raw data, develop data models, and proficiently use SQL and Python for ETL processes.

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.

As a Data Engineer, you will build scalable ETL/ELT pipelines, optimize data structures, and develop impactful Power BI solutions for stakeholders.

The Data Engineer will design and maintain data models, develop BI solutions, integrate data, and optimize data flows while collaborating with stakeholders.

Design, develop, and maintain ELT/ETL Snowflake data warehouse solutions; implement data standards, CI/CD, monitoring, and production support; collaborate with product, business, and vendors; mentor junior developers and improve data engineering practices.

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 and build a greenfield, multi-layer data warehouse and semantic metrics layer; separate analytics from OLTP; ensure data quality, traceability, tenant isolation, and high SLAs; enable linking of structured records to drone imagery/video; deliver self-service data access, observability, and infrastructure automation to scale across products and tenants.

Design, build, and operate scalable cloud‑native data platforms and reliable ETL/ELT pipelines (batch and streaming). Optimize performance, ensure production readiness (security, testing, observability), troubleshoot/operate systems, collaborate with analytics/AI teams, and mentor junior engineers to deliver enterprise data solutions.

The Senior Tableau Consultant focuses on data visualization development, project management, mentoring, and supporting business requirements using Tableau. Responsibilities include dashboard architecture, user configuration, and training.

Design, build, and maintain cloud-based data infrastructure and pipelines; develop, train, and deploy AI/ML models; collaborate with cross-functional teams to productionize models, ensure data quality, scalability, and monitoring.

The role involves leading GCP data engineering initiatives, developing scalable data platforms, implementing data governance, mentoring teams, and enhancing enterprise AI capabilities.

The Lead GCP Data Engineer will design and implement scalable cloud data platforms, optimize data processing pipelines, and lead engineering teams for enterprise AI and analytics initiatives.

Design, develop, and maintain scalable analytics solutions on the Looker BI Platform, focusing on LookML modeling and collaboration with data engineers and stakeholders.

The Senior Data Engineer will build and maintain data ingestion pipelines, manage FHIR serialization, and work within a spec-driven development framework, ensuring operational stability and high data quality for healthcare data workloads.

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.

Design, build, and maintain scalable Azure-based data pipelines and systems (Databricks, Data Lake, Synapse, ADF). Collaborate with cross-functional teams, ensure data quality and integrity, optimize performance, monitor and troubleshoot systems, and stay current with industry trends to improve solutions.

Design, build, and optimize end-to-end ETL/ELT data pipelines on an Azure-based platform. Translate business needs into data models, KPIs, and dashboards (Power BI/Tableau). Ensure data quality, documentation, metadata/lineage, and governance while collaborating across engineering, analytics, and data governance teams in an agile environment.

Design, build, and deploy agentic AI solutions end-to-end for enterprise customers. Lead architecture decisions, integrate LLMs and data platforms, resolve production issues, build observability and KPI dashboards, mentor engineers, and codify reusable patterns for the Agentforce platform to drive measurable business outcomes.

The role involves data modeling, troubleshooting complex issues, optimizing storage architectures and mentoring junior engineers while working with GCP and Python.

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.