Top Data Engineer Jobs in Mumbai

Designs and executes data, ETL, business intelligence, and database testing. Validates end-to-end data flows using SQL and BigQuery within GCP, performs data quality checks, documents defects, and supports root-cause analysis. Collaborates with developers, analysts, and stakeholders to improve test automation, delivery governance, and testing efficiency. The role requires 5–7 years of quality engineering or data testing experience, with preferred knowledge of cloud platforms, Python, dbt, and DevOps.

Leads enterprise Oracle database migration and modernization to public cloud platforms. Administers Oracle 19c environments, Data Guard, ASM, backup and recovery, tuning, patching, upgrades, and high availability operations. Automates provisioning and lifecycle processes using Linux, shell scripting, and Python, while supporting cloud security, governance, monitoring, and incident resolution. Collaborates with application stakeholders and contributes to Agile delivery, documentation, and self-service database services.

Lead the migration and modernization of enterprise Oracle databases to public cloud platforms. Administer Oracle 19c environments, Data Guard, ASM, backup and recovery, tuning, patching, and upgrades in a 24/7 setting. Develop Linux, shell, and Python automation; support cloud security, governance, lifecycle management, monitoring, and self-service provisioning. Collaborate with application teams, resolve incidents and alerts, and maintain operational documentation using Agile and DevOps practices.

Lead migration and modernization of enterprise SQL Server databases to public cloud platforms. Administer mission-critical SQL Server environments, including Availability Groups and Distributed Availability Groups, while managing tuning, capacity planning, backup and recovery, patching, upgrades, monitoring, and production support. Automate infrastructure and database operations with PowerShell and Terraform, maintain cloud security standards, resolve critical incidents, and collaborate with application stakeholders in an Agile environment.

Design, build, and maintain enterprise ETL/ELT pipelines in Microsoft Fabric; ingest and transform data from diverse sources into dimensional star schemas; optimize performance and cost; implement data quality checks and monitoring; document architecture and pipelines; collaborate with analysts, stakeholders, and global engineering teams to deliver Power BI-ready analytics datasets.

Design and build scalable data and AI infrastructure for production LLM applications. Responsibilities include developing RAG systems, embedding and vector retrieval pipelines, AI agents, LLM evaluation and observability frameworks, and batch or streaming data platforms using Databricks, Spark, Snowflake, Delta Lake, and Airflow. The role also involves optimizing distributed workloads, establishing data governance and quality practices, and partnering with engineering and product teams to productionize AI capabilities.

Designs and maintains scalable data pipelines using AWS, Spark, Kafka, Airflow, and related technologies. Collaborates with product, data science, and business teams; develops reporting dashboards; documents data flows and runbooks; optimizes workflows; troubleshoots data processing issues; and supports Agile delivery and CI/CD practices.

Develop and maintain Python-based API data ingestion services for GCP. Build secure, scalable, fault-tolerant pipelines with authentication, incremental loading, validation, reconciliation, orchestration, monitoring, and alerting. Manage data quality, PII masking, CI/CD, production troubleshooting, performance optimization, SLAs, data contracts, technical designs, mappings, and operational documentation. Collaborate with stakeholders to define support models and ensure reliable ingestion from internal and external APIs.

As a Senior Data Engineer, you will develop and maintain data pipelines for healthcare data, ensuring data quality, reliability, and adherence to enterprise standards while collaborating with architects and stakeholders.

The Senior Data Engineer will develop data ingestion, transformation, and enrichment pipelines, ensuring data quality and reliability. Responsibilities include creating Bronze and Silver data layers, using SQL and dbt for implementation, and collaborating with architects and stakeholders to align data solutions with enterprise standards.

As an Azure Data Engineer, you will innovate and maintain data engineering services for Fortune clients, develop software for large projects, and utilize Azure components for solutions.

Designs and leads enterprise business intelligence and analytics solutions using Power BI, Microsoft Fabric, Snowflake, Azure Data Factory, and modern data architectures. Responsibilities include developing semantic models, datasets, dashboards, data pipelines, data products, and scalable data platforms; implementing governance, quality, lineage, and metadata practices; enabling self-service analytics; optimizing performance; evaluating emerging tools such as Promethium; and advising stakeholders while mentoring data teams.

Designs and maintains enterprise data architecture, including conceptual, logical, and physical data models. Establishes data standards, metadata conventions, governance policies, security frameworks, and data dictionaries. Leads Master Data Management initiatives to improve consistency and integrity across platforms. Collaborates with business, analytics, application, and engineering teams, while supporting database, data warehouse, integration, and ETL pipeline efforts aligned with business, regulatory, security, and performance requirements.

Build, optimize, and maintain scalable AWS-based data pipelines and data lakes (Snowflake/Databricks). Implement streaming ingestion (Kinesis/Kafka), CDC, automated data quality checks, and ML-ready feature stores. Collaborate with Product, ML, and Analytics teams, own pipeline QA, observability, and participate in on-call rotations to resolve production data issues.

Build, monitor, optimize, and maintain ETL/ELT pipelines using Google Cloud technologies. Develop complex SQL transformations, design dimensional data models, improve query performance and cloud cost efficiency, and ensure data integrity. Collaborate with analysts and stakeholders to translate SaaS and insurance metrics into technical solutions. Participate in code reviews, documentation, troubleshooting, and data warehouse lifecycle management while supporting US business hours.

Design and develop enterprise Master Data Management solutions using Profisee, Informatica MDM, or Reltio. Build scalable ingestion, integration, transformation, ETL/ELT, and publishing pipelines; implement cleansing, matching, deduplication, survivorship, hierarchy, and data quality rules. Integrate MDM platforms with databases, APIs, data lakes, warehouses, and applications. Troubleshoot production issues, maintain mappings and documentation, collaborate with stakeholders, support delivery activities, and mentor junior data engineers.

Design, build, and optimize scalable GCP data pipelines and BigQuery data warehouses. Develop ETL/ELT workflows, implement data modeling, ensure data quality, optimize query performance and costs, maintain orchestration with Cloud Composer/Dataflow/Pub‑Sub, and automate deployments with CI/CD while enforcing security and governance.

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.

Develop and maintain reliable data pipelines, ELT processes, workflow orchestration, custom data connectors, and CI/CD pipelines using Airflow, Python, PySpark, Hive/Trino, SQL, and Snowflake. Collaborate with cross-functional teams, monitor and troubleshoot data workflows, validate and test data, document technical processes, and support data governance.

Leads end-to-end data products from ingestion and transformation through semantic modeling, Power BI reporting, documentation, governance, and user adoption. Builds Microsoft Fabric pipelines, Lakehouse or Warehouse architectures, dimensional models, and secure, optimized Power BI solutions. Analyzes data, supports advanced analytics and AI-assisted reporting, establishes quality and CI/CD practices, collaborates with cross-functional stakeholders, and manages data engineers through objectives, reviews, and development.

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.

Define company-wide database and data infrastructure strategy, design scalable distributed architectures, and lead migrations, sharding, multiregion deployments, and reliability improvements. Establish standards for data modeling, replication, consistency, capacity planning, observability, disaster recovery, and lifecycle management. Evaluate technologies, optimize infrastructure economics, partner with engineering leadership, influence technical direction, and mentor engineers across teams.

Provides technical leadership for scalable, secure data platforms, pipelines, and products supporting analytics, real-time data, and AI use cases. Responsibilities include designing ETL strategies, data architectures, integrations, cloud solutions, and consumption patterns; mentoring engineers; setting standards; improving automation and reliability; partnering with cross-functional teams; managing technical roadmaps; and participating in on-call support.

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.

Build and operate production-grade AI data pipelines and financial knowledge services for equities content. Responsibilities include processing text, tables, documents, and multimedia; creating knowledge bases and search indexes; engineering RAG retrieval and citation systems; integrating LLMs and NLP models; managing orchestration, APIs, quality, cost, observability, reliability, and scalable production operations.