Top Data Engineer Jobs in Mumbai

Build and own scalable Snowflake data infrastructure, ETL pipelines, curated datasets, and data quality frameworks supporting financial analytics. Partner with modeling experts to translate requirements into production-ready designs, optimize performance and costs, standardize deployments, and meet SLAs. Use AI-assisted engineering tools responsibly for coding, testing, troubleshooting, and documentation while maintaining privacy, security, and governance standards.

Lead design and implementation of AI-driven data platforms and pipelines. Build production Python and PySpark applications, REST APIs, and Databricks/Snowflake solutions. Architect ETL on AWS (Glue, S3), enable GenAI/LLM workflows (RAG, embeddings, multi-agent orchestration), ensure governance, performance tuning, cost optimization, and platform administration. Mentor teams and enforce best practices for observability, testing, and secure AI deployment.

Build and maintain enterprise data pipelines, ELT processes, data models, reporting structures, and analytics solutions. Cleanse, deduplicate, normalize, and govern data across warehouses and data lakes, including Snowflake. Develop dashboards and business intelligence models, ensure data quality and compliance, apply business logic, and collaborate with technical and non-technical stakeholders to deliver accurate insights and improve operational performance.

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.

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.

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.

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.

Designs, develops, and maintains scalable data architectures, ETL pipelines, data lakes, warehouses, data models, and data catalogs. Builds data solutions using Azure Data Factory, Azure Data Lake, Databricks, Scala, .NET, Python, and PySpark. Partners with data scientists, business stakeholders, analysts, and architects to deliver analytics-ready data products, establish governance and standards, mentor professionals, evaluate emerging tools, and support strategic enterprise initiatives.

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.

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.

Partner with investment, operations, and finance teams to translate business needs into BI dashboards, reporting, automation, and AI-enabled analytics using Power BI and SQL. Identify reporting gaps, capture requirements, and support adoption of AI-driven insights while liaising between business stakeholders and technical teams.

Design, develop, and maintain data solutions and scalable data pipelines. Ensure data quality and governance, implement ETL processes, troubleshoot complex data issues, and mentor/lead team members while collaborating across teams.

Design, build, and optimize scalable data pipelines and Redshift data models; ingest from MySQL and real-time Kafka streams; migrate on-prem systems to AWS using Airflow, dbt, Lambda, Glue, and S3; embed data quality, observability, and security; collaborate with cross-functional teams to support mission-critical payments analytics.

Operate, maintain, and enhance data pipelines that validate, transform, and enrich policyholder data for actuarial modelling. Investigate and resolve data issues, implement checks and controls, collaborate with suppliers and stakeholders, maintain documentation and runbooks, and support data governance and audit evidence delivery.

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 Snowflake-based data products and ELT pipelines (dbt). Implement data quality, observability, CI/CD, Terraform IaC, and event-driven integrations (Kafka). Deliver governed datasets for analytics and real-time use, mentor junior engineers, collaborate with stakeholders, and support AI/advanced analytics initiatives.

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.

Lead design and implementation of end-to-end data pipelines and migrations, liaise with stakeholders, automate ingestion/transformation of batch and real-time structured and unstructured data, enforce data governance and quality, document data models and rules, migrate pipelines to AWS PaaS, drive process improvements, and manage full software lifecycle for ETL solutions.

Design, build, and maintain data architecture and pipelines using Azure, Databricks, and on-prem SQL. Deliver data ingestion, transformation, modeling, warehousing, and self-service data products. Collaborate with analysts, architects, and engineering leads to ensure best practices, CI/CD, documentation, and stakeholder alignment.

Design, build, and maintain scalable Python-based data pipelines and backend services. Use OOP principles, orchestrate workflows with Apache Airflow, and model/optimize data in Snowflake. Implement data validation and quality checks, monitor and troubleshoot pipeline performance, collaborate with analysts and data scientists, and document data workflows and architecture.

Design, build, and maintain scalable ETL/ELT data pipelines and cloud data platforms on AWS/GCP using PySpark and SQL. Implement data quality, governance, performance tuning, and cost optimization while collaborating with analysts and data scientists and monitoring production systems.

Design, build, and optimize scalable data pipelines using PySpark; orchestrate workflows with Apache Airflow; use SQL and Cloudera Hadoop (HDFS, Hive, Impala); ensure data quality, integrity, and security; collaborate with data scientists and analysts; troubleshoot, optimize jobs, and implement version control and CI/CD best practices.

Design, develop, and maintain scalable ETL/ELT pipelines using Python and PySpark. Optimize and manage large datasets in Google BigQuery, ensure data quality, build job orchestration with Airflow, write efficient SQL, and collaborate with data scientists, analysts and product teams to deliver analytics-ready data and documentation.