Top Data Engineer Jobs in Mumbai

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.

Design and maintain batch and real-time data pipelines, data lakes, warehouses, marts, and reusable dbt models using Spark, Kafka, Maxwell, S3, Trino, BigQuery, and related technologies. Optimize distributed processing, data models, queries, storage, reliability, and costs. Establish data quality, observability, governance, automation, and CI/CD practices while partnering with Product, Engineering, Analytics, Data Science, and business stakeholders.

Build and operate scalable financial market data pipelines for Binance stock products and AI applications. Responsibilities include sourcing, ingesting, modeling, processing, storing, and serving real-time and historical data; designing multi-market data frameworks; optimizing Flink pipelines; and establishing quality, reconciliation, monitoring, lineage, and recovery systems. The role also evaluates vendors and data sources, defines data contracts, and collaborates with trading, product, AI, legal, and compliance teams.

Build and operate scalable financial market data infrastructure, including source evaluation, ingestion, cleansing, modeling, real-time and batch processing, quality governance, reconciliation, monitoring, and data services. Develop Flink-based pipelines supporting trading, research, and AI applications. Partner with product, trading, compliance, procurement, and engineering teams to define data contracts, source strategies, and consistent financial data semantics across markets.

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.

Build, monitor, optimize, and maintain cloud-based ETL/ELT pipelines supporting business intelligence and reporting. Develop high-performance SQL transformations, manage data warehouse schemas, tune query and pipeline performance, and optimize cloud costs. Collaborate with analysts, data scientists, actuaries, and architects to translate insurance and SaaS requirements into scalable technical solutions. Participate in code reviews, documentation, troubleshooting, and data integrity initiatives.

Design, build, and support production-grade AWS data pipelines that transform operational data into secure, high-quality, AI-ready datasets. Responsibilities include distributed data processing, Parquet curation, privacy-preserving transformations, orchestration, data quality monitoring, schema management, CI/CD, infrastructure as code, metadata and lineage management, troubleshooting, and reliable backfills.

Build production-grade agentic AI systems that reason, search, and execute workflows. Develop RAG pipelines, AI agents, semantic search layers, data processing solutions, dimensional data models, and scalable AI workloads. Work with structured and unstructured data, LLMs, vector databases, cloud infrastructure, CI/CD, and ML observability. Responsibilities include fine-tuning models, prompt engineering, optimizing context windows, and deploying reliable AI applications.

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.

Design, develop, and maintain scalable Microsoft Fabric and Azure data solutions. Build Lakehouse, Data Warehouse, OneLake, ETL/ELT pipelines, data transformations, and integrations using Python, PySpark, SQL, and Fabric tools. Implement medallion architecture, optimize data models and workloads, establish data quality monitoring, and support CI/CD deployments. Collaborate with analytics and business teams to deliver enterprise-scale data platforms.

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.

Principal Data Engineer responsible for developing and supporting Databricks pipelines, integrating heterogeneous healthcare data sources, modeling unified data products, and ensuring data quality and availability. The role partners with business stakeholders, product teams, data engineers, and clinical leaders to gather requirements, create datasets, support ad hoc analytics, enable machine learning and AI use cases, and assist with acquisition integrations. Extensive healthcare data, cloud, SQL, PySpark, and Python experience is required.

Senior Data Engineer role at Priceline within a hybrid work environment. The provided description focuses on company culture, benefits, inclusion, and application procedures, without detailing specific responsibilities, technologies, qualifications, salary, travel, or clearance requirements.

Build and optimize large-scale AWS data engineering solutions, including batch and streaming pipelines, Lakehouse architectures, and Redshift analytics. Develop with DBT, SQL, Python, and Spark/PySpark; optimize Apache Iceberg and Aurora PostgreSQL. Implement data modeling, monitoring, observability, quality controls, CI/CD, and production support while collaborating with stakeholders. Preferred experience includes AWS certification, Terraform, Kafka, ClickHouse, GenAI coding assistants, Docker, Kubernetes, and telecom domains.

Build and optimize scalable data pipelines, ingest structured and streaming data, develop Redshift data models, and optimize SQL for analytics. Contribute to AWS cloud migration, using Airflow, dbt, Kafka, and AWS services. Partner with global teams to deliver reliable, observable, secure data solutions in a mission-critical payments environment while improving governance, quality, and platform standards.

Build and maintain scalable data products using Snowflake, dbt, and Airflow. Responsibilities include dimensional data modeling, SQL transformations, pipeline orchestration, data quality testing, exploratory analysis, SLA monitoring, documentation, and stakeholder requirement gathering. Partner with analytics, product, engineering, and business teams to deliver reliable, production-ready datasets while following software engineering, CI/CD, testing, and deployment practices.

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.

Build and maintain large-scale Snowflake data platforms and pipelines, including AWS and legacy-platform migrations. Develop SQL, Spark, PySpark, and Python transformations; implement batch and streaming workflows; optimize Snowflake performance; and apply security, governance, and data-sharing capabilities. Support migration validation, troubleshooting, monitoring, production operations, SDLC, CI/CD, and stakeholder collaboration while using Snowflake AI and migration tools.