Top Data Engineer Jobs in Mumbai

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.

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.

Creates and manages data connections, validates and transforms external portfolio data, and normalizes datasets into Clearwater’s standardized format. Automates process monitoring and reporting, integrates APIs and FTPs, and improves auto-reconciliation through feed analysis and solution development. The role supports operational and development requests, troubleshoots data issues, documents workflows, communicates technical concepts to stakeholders, and participates in light on-call support.

Designs and maintains automated data quality frameworks, validation controls, anomaly monitoring, and source-to-target reconciliation processes for large-scale financial datasets. The role validates data from market-data providers, embeds quality controls into ETL/ELT pipelines, supports new dataset onboarding, and performs profiling and root-cause analysis. Requires advanced Python, SQL, and PySpark skills, financial data expertise, and exposure to AWS-based platforms and AI productivity tools.

Designs, develops, and supports scalable cloud data pipelines, transformations, data models, and reporting datasets. Partners with engineering, business intelligence, and business teams to deliver reliable, governed data solutions. Monitors and troubleshoots production processes, improves performance and reliability, contributes to data quality and documentation standards, and participates in code reviews and engineering best practices.

Leads the architecture, delivery, modernization, and operations of enterprise data platforms, warehouses, databases, and engineering solutions. Designs ETL/ELT and CDC pipelines, manages cloud and database environments, improves reliability, security, scalability, and cost efficiency, and drives migrations and strategic initiatives. Provides technical leadership, mentors engineers, coordinates cross-functional projects, manages incidents and root-cause analysis, supports capacity planning, and collaborates with security, cloud, SRE, analytics, and business teams.

Deploy and onboard structured and unstructured data products onto BlackRock’s Data Platform as a Service. Responsibilities include defining schemas, ownership, SLAs, quality, and governance; troubleshooting pipelines; developing reusable ingestion connectors, templates, and accelerators; supporting platform engineering; enabling partner teams through workshops and documentation; and contributing AI-assisted onboarding workflows using schema inference, metadata mapping, agents, and automation.

Design, develop, and maintain data solutions, pipelines, ETL processes, and data warehouses. Ensure data quality, support data migration and integration, troubleshoot issues, and optimize workflows. Collaborate with stakeholders and multiple teams to translate data requirements into technical solutions, make team-level decisions, uphold data governance practices, and mentor junior engineers. The role requires expertise in Data Build Tool and approximately eight years of relevant experience.

Design and maintain scalable pipelines for visual and voice data, including ingestion, processing, validation, testing, observability, and automation. Build cloud, on-premise, and containerized data infrastructure; optimize storage, retrieval, and caching for large media assets; debug complex data issues; and collaborate with data scientists, operators, and product teams to support machine learning workflows.

Designs, builds, and supports cloud data engineering, ETL, SQL, and data warehouse solutions primarily on GCP. The role integrates enterprise data, supports platforms and scheduling tools, partners with technical and business stakeholders, leads small projects, drives automation and continuous improvement, and trains team members. Required expertise includes Python, SQL, BigQuery, Composer, Cloud Functions, dbt, cloud storage, data warehousing, and data governance.

Leads and manages enterprise data engineering teams, overseeing managers, staffing, stakeholder relationships, vendor partnerships, operating-model improvements, data quality, and strategic initiatives. Provides technical guidance on cloud data platforms, data architecture, ETL/ELT, data warehousing, standards, and Agile delivery while building a culture of learning, collaboration, and accountability.

Designs, develops, and optimizes scalable data pipelines, platforms, and models using Snowflake, dbt, Azure, and Databricks. Integrates and harmonizes multiple data sources, establishes data-quality and governance practices, applies security controls, and enables analytics and reporting. The role also translates stakeholder requirements into sustainable data solutions, optimizes performance and cost, monitors workflows, and contributes to engineering standards and continuous improvement.

Designs, builds, and supports GCP-based data engineering, ETL, SQL, and data warehouse solutions. Maintains scheduled jobs, analyzes business requirements, troubleshoots production issues, and partners with architects, analysts, vendors, and IT teams. Leads small initiatives, supports enterprise projects, automates processes, trains users, and drives continuous improvement across distributed global teams.

Designs, builds, and maintains batch and real-time data pipelines, ingestion systems, cloud-native ETL/ELT workflows, and lakehouse solutions for client projects. Integrates diverse data sources, applies data quality and observability practices, and prepares modeled data for analytics, AI, ML, and GenAI applications. Uses DataOps practices including Git, CI/CD, testing, documentation, and code reviews while collaborating with consulting teams and clients.

Support, maintain, troubleshoot, and optimize production Databricks applications, Spark jobs, SQL queries, Delta Lake pipelines, clusters, workflows, and workspace configurations. Implement data quality checks, logging, alerting, governance, security, and cost-management practices. Collaborate on enhancements and bug fixes, reduce technical debt, document operational processes, and participate in EST-aligned support rotations.

Design, develop, and optimize secure, scalable cloud data platforms and ETL/ELT pipelines across AWS, Azure, and GCP. Write Python and SQL, implement data governance and quality controls, optimize performance and costs, and support analytics and BI layers. Contribute to architecture, pre-sales demos, proofs of concept, and client discussions. Mentor junior developers, conduct code reviews, and collaborate with architects, QA, DevOps, and business stakeholders.

Designs and develops scalable cloud data platforms and ETL/ELT pipelines using Python and SQL. Optimizes performance and costs, implements governance, security, quality, and compliance controls, and supports analytics consumption. Collaborates with architects and stakeholders, contributes to pre-sales prototypes and demos, mentors junior developers, conducts code reviews, and recommends improvements across AWS, Azure, and GCP environments.

Maintains, cleans, manipulates, and secures operational and analytics databases. Designs scalable data pipeline architecture, builds complex data sets and analytics tools, automates manual processes, improves infrastructure, troubleshoots database issues, and supports software engineering, data science, analytics, product, design, and executive stakeholders.

Maintains, cleans, manipulates, and secures operational and analytics data. Designs scalable data pipeline architectures, builds complex data sets and analytics tools, automates manual processes, improves infrastructure, troubleshoots database issues, and supports data, product, design, analytics, and data science teams with technical data needs.

Lead the design, development, and evolution of a centralized Snowflake data platform integrating data from 30–40 upstream systems. Build scalable pipelines, ETL/ELT processes, data models, quality controls, monitoring, and analytics-ready data products. Partner with engineering, product, data, reporting, and controls teams to resolve data issues, improve platform performance and resilience, and support AI/ML use cases. Provide technical leadership and mentorship while promoting strong coding, testing, documentation, and agile delivery practices.

Design, build and maintain scalable batch and real-time data pipelines and data products on AWS to ingest security telemetry, enable analytics/ML, ensure data quality and observability, integrate with enterprise security tooling, and drive DevSecOps/automation for the Group Security Data Platform.

As a Data Engineer Intern, you will write efficient code, learn new technologies, collaborate with teams, and deliver data products to improve decision-making.

Build and maintain batch and streaming data pipelines across AWS, Azure, and GCP using Databricks, Spark, PySpark, SQL, and Delta Lake. Implement medallion architecture, CDC, SCD, data quality, governance, security, metadata, and lineage controls. Configure storage, orchestration, CI/CD, monitoring, troubleshooting, and performance optimization. Collaborate with architects, data scientists, engineers, analysts, and product teams while documenting pipelines, transformations, testing, and operational procedures.

Design, build, and maintain scalable data pipelines and ETL/ELT workflows using Azure Data Factory, Databricks, PySpark, Python, and SQL. Transform large datasets, optimize data ingestion and queries, monitor pipeline performance, resolve data quality issues, support data modeling, document engineering standards, and contribute to cloud data infrastructure improvements. The role also involves collaboration with architects and senior engineers and requires familiarity with Azure Synapse, Delta Lake, governance, and security.

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.