Top Data Engineer Jobs in Mumbai

Design, build, and operationalize AI-enabled data pipelines and production-grade AI applications using Python and Snowflake. Prepare and transform large datasets, develop features and embeddings, support deployment and monitoring of ML/LLM models, optimize Snowflake for AI workloads, and collaborate with data science and engineering teams to deliver scalable APIs and RAG-enabled data workflows.

Design, develop, and support Python-based data pipelines, manage data transfers and onboarding, monitor pipeline health and SLAs, provide L2/L3 production support, troubleshoot incidents, document systems, and partner with stakeholders to translate requirements into scalable, automated data engineering solutions.

The Azure Data Engineer will build data pipelines and dataflows, implement security modules, and work with Azure services while supporting business requirements and data analysis.

We are looking for a Data Engineer with skills in Python, SQL, and Azure technologies to develop Big Data applications. Responsibilities include working with Spark and Docker to handle large scale data processing and analytics.

The Data Engineer role involves analyzing raw data, building data systems, conducting complex analyses, and enhancing data quality by collaborating with data scientists and architects.

This role involves collaborating with users to build data solutions, managing AWS data technologies, and automating workflows for efficiency.

The Azure Data Engineer will implement big data pipelines using Azure technologies, collaborate with stakeholders, and utilize tools like Data Factory and Databricks.

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.

Responsible for designing and developing a GCP data ecosystem, creating data pipelines, optimizing data architectures, troubleshooting issues, and mentoring junior engineers.

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.

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.

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.

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, 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.

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.

Design, develop, and optimize AWS Glue ETL pipelines integrating data from S3, RDS, Redshift, and other AWS services. Transform data into analytics-ready formats, ensure data quality and security, tune performance, orchestrate workflows (Step Functions/Airflow), monitor jobs, and collaborate with analysts and stakeholders to deliver scalable data lake and warehouse 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 ETL workflows using Informatica, BDM, and Data Integrator. Collaborate with architects and analysts to translate requirements, optimize performance, ensure data quality, and document mappings for large structured and unstructured datasets supporting BI and data warehousing.

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.

The role involves designing and building data infrastructure, developing ETL/ELT applications, collaborating with teams, and improving engineering processes for analytics and AI capabilities.

The Senior Analyst - Data Engineer will develop and maintain data pipelines, ensure data quality, and support data-driven insights using Databricks and cloud technologies.

As a Data Engineer, you will develop and manage data pipelines, ensuring reliable data ingestion and delivery while optimizing for simplicity and performance.