Top Data Engineer Jobs in Mumbai

Design, build, and optimize end-to-end data pipelines for large structured and unstructured data. Implement near-real-time ETL, data validation, monitoring, and performance optimization. Collaborate with stakeholders, document designs and workflows, and provide technical guidance to the team.

Design, implement, and optimize Apache DolphinScheduler workflows and ETL pipelines. Integrate with Hadoop, Spark, Flink, Hive, and Kafka. Develop custom plugins, troubleshoot performance and scalability issues, monitor job execution and resource allocation, and maintain CI/CD, version control, and infrastructure automation while collaborating with data engineers and DevOps teams.

Design, build, and optimize PySpark applications and ETL pipelines to process large-scale datasets from relational, NoSQL, file, and streaming sources. Ensure data quality, implement error handling, and collaborate with analysts, scientists, and architects to deliver performant data solutions.

Design, build, optimize, and maintain high-performance Spark-based data pipelines using Scala/Java and Hive on Hadoop/CDP. Own full project lifecycle, enforce coding best practices, troubleshoot Spark/Hive/YARN performance, and collaborate with stakeholders to deliver scalable data solutions.

Design, develop, test, and deploy high-performance Spark/Scala data processing applications and ETL pipelines on Cloudera (CDH). Optimize Spark and Cloudera platform performance, integrate HDFS/Hive/Impala/HBase/Kafka, ensure data integrity/security, and implement version control and CI/CD for production pipelines.

Design, model, and optimize relational and analytical data structures for OLTP and data warehouse systems. Build ER diagrams, define modeling standards, manage metadata with tools like Erwin/ER Studio, and drive data cataloging/lineage using Alation/Collibra. Perform performance tuning, sandbox POCs, handle time-series/history, and collaborate with engineers, architects, and product teams while documenting and enforcing governance.

Design, configure, and support backend software and APIs; manage configuration files and environments; test and document API endpoints; collaborate on system data flow; participate in large-scale live data migrations (billions of records) into FinCloud; produce documentation and troubleshooting guides; work in agile teams and support integration and observability efforts.

Design, build and maintain cloud-oriented big data solutions for ingestion, processing, cleaning and exposition. Improve data quality, implement ETL and data-warehouse architectures, and collaborate with DDS/Data Foundation teams to ensure platform reliability.

Senior Java engineer responsible for building data lake and big data solutions (preferably on Spark) with cloud experience (Azure/AWS/GCP), REST services, OOA/OOD and JEE design patterns, PostgreSQL expertise, TDD practices, and working in Agile teams. Optional: front-end (Angular/React), Docker/Kubernetes, investment banking domain knowledge.

Lead migration of a legacy data warehouse to Microsoft Fabric, design and implement a medallion (lakehouse) architecture, perform Fabric fitment assessments, define target cloud architecture and operating model, create migration roadmaps, and deliver a Proof of Value/MVP while collaborating with customer engineering teams.

Lead AI/ML initiatives as an individual contributor: design and implement ML solutions, develop and maintain code, define architecture, guide data infrastructure, and collaborate with cross-functional teams to solve business problems.

Build and operate automated data quality checks, monitoring, alerting, and dashboards; integrate quality into data pipelines and governance tooling; automate anomaly detection and support auditors, analysts, and stewards to surface issues in near-real-time.

The Senior Data Engineer will build and optimize data pipelines in Azure Databricks, migrate existing pipelines from Azure Synapse, and design scalable data models to support a financial services client's data platform modernization.

Responsible for guiding teams in utilizing technology effectively, mentoring colleagues and devising advanced solutions to meet client challenges.

As a Senior Engineer in Data Analytics, you'll work on challenging problems using cutting-edge technology alongside talented colleagues to drive innovative solutions.

The Senior Staff Engineer, Data Science role involves guiding team direction, solving complex challenges for clients, and leveraging technical expertise to innovate solutions.

The Principal Engineer, Data Science role involves leading data science projects and providing strategic insights using extensive IT experience.

As a Senior Engineer for Strategic Data Solutions, you will manage data warehousing, reporting, and analytics for internal operations and external partners. Responsibilities include collaborating with business units, providing insights, and ensuring high-quality data reporting.

The Principal Data Science Engineer will leverage statistical analysis and machine learning to optimize airline revenue performance and customer experience, collaborating with teams to develop data models and algorithms, and support product development cycles.

The role involves building and scaling the Acceldata Open Data Platform, focusing on observability, coding, debugging, and collaborating with teams and the open-source community.

The Snowflake Senior Data Engineer will lead projects in data engineering, utilizing Snowflake and various AWS services to optimize data workflows and storage, while also guiding the team and collaborating with stakeholders.

Design, build, and optimize scalable data pipelines and models using Spark, Kafka, Airflow, dbt, and SQL across GCP and AWS. Ingest and normalize marketing and analytics data, ensure data quality and governance, support analytics and reporting, and collaborate with cross-functional global teams.

Design, operate, and secure scalable batch and streaming data pipelines and platforms. Own ingestion, transformation, serving, data quality, and access controls. Partner with product, risk, compliance, and InfoSec to ensure production reliability, correctness, and audit readiness for financial data.

Design, build, and scale cloud-native Python backend services and data-backed applications. Develop RESTful APIs, microservices, and asynchronous event-driven systems. Deploy and operate production workloads on Kubernetes and at least one public cloud (AWS or Azure). Work with cloud data warehouses (Snowflake or Databricks) and relational databases, ensuring scalable, reliable distributed systems.

As a Principal Engineer for Data Infrastructure, you'll oversee our databases and data warehousing, optimize performance, and lead technology integrations to enhance data processing capabilities.