Top Data Engineer Jobs in Mumbai

Build, deploy, and support production-grade data pipelines and platforms for structured and semi-structured datasets. Develop Snowflake data models and advanced SQL ELT transformations, implement workflow orchestration and data-quality controls, and improve reliability, observability, lineage, and performance. Partner with product teams and data specialists, troubleshoot production issues, modernize legacy pipelines, and deliver maintainable solutions using Python and cloud technologies.

Builds and maintains enterprise data platform frameworks, including automated pipelines, ingestion, processing, orchestration, data quality, testing, and performance improvements. The role uses Python, SQL, Airflow, Snowflake, dbt, Azure, and streaming technologies. Responsibilities include collaborating with stakeholders across the SDLC, supporting platform users, conducting root cause analysis, providing L2/L3 support, contributing to complex system design, and ensuring scalable, reliable production operations.

Design and build scalable cloud-based data solutions and maintain ETL pipelines into data warehouses and lakes. Implement data quality and validation processes, optimize processing and storage performance, and apply dimensional modeling and Databricks Medallion Architecture. Collaborate with data teams, product owners, engineering leads, analysts, and stakeholders while following data engineering standards and best practices.

Build and maintain scalable cloud-based data solutions, including ETL pipelines, data warehouses, data lakes, and data quality processes. Optimize data processing and storage, apply dimensional modeling and medallion architecture, and collaborate with engineering, product, modeling, analytics, and business stakeholders. Use Python, PySpark, SQL, Databricks, AWS services, and scheduling technologies to deliver reliable analytics infrastructure and support business insights.

Design and build scalable cloud-based data solutions, develop and maintain ETL pipelines, ensure data quality, optimize processing and storage, and support analytics initiatives. The role uses Python, PySpark, SQL, Databricks, AWS services, dimensional modeling, and scheduling technologies while collaborating with engineering, data modeling, analytics, product, and business stakeholders.

Design and build scalable cloud-based data solutions, develop ETL pipelines for data warehouses and lakes, ensure data quality, optimize storage and retrieval, and collaborate with data architects, modelers, product managers, analysts, and stakeholders. The role also guides junior data engineers and applies AWS, Databricks, Python, PySpark, SQL, Airflow, and data modeling technologies.

Design and build scalable cloud-based data solutions, develop ETL pipelines for warehouses and data lakes, enforce data quality, optimize processing and storage, collaborate with data and product teams, and mentor other engineers. The role requires expertise in Python, PySpark, SQL, Databricks, AWS services, data modeling, medallion architecture, and scheduling technologies.

Design, develop, and maintain data solutions, pipelines, and ETL processes using GCP Vertex AI. Ensure data quality, support data migration and warehousing, optimize workflows, and collaborate across teams. Serve as a subject-matter expert, manage team decisions, provide cross-team solutions, and lead knowledge-sharing and process improvement initiatives.

Develop and maintain batch and streaming data pipelines on Google Cloud. Build complex data transformations, orchestrate ETL workflows, perform data migrations, and maintain data warehouses and data lakes. The role requires expertise in BigQuery, Composer, GCS, Cloud SQL, Dataflow, Dataproc, IAM, Python, SQL, and PySpark, along with strong database, data modeling, communication, and problem-solving skills.

Design, develop, and maintain scalable data solutions, pipelines, and ETL processes. Ensure data quality, accuracy, and consistency across systems while supporting data migration and deployment. Lead architecture decisions, collaborate with stakeholders and multiple teams, troubleshoot data issues, and mentor junior engineers. Serve as a subject matter expert and contribute to team leadership and technical decision-making.

Design and optimize scalable ETL/ELT pipelines using Databricks, PySpark, SQL, GCP, BigQuery, and Delta Lake. Build agentic workflows that automate data validation, troubleshooting, issue identification, and operational processes. Develop reusable frameworks, implement data quality and production controls, optimize workloads, and support testing, deployment, monitoring, and production support. Collaborate with business, data engineering, and platform teams to identify automation opportunities.

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.

Design, develop, and maintain data solutions and ETL pipelines using Snowflake and related tools. Ensure data quality, lead cross-functional data strategy, mentor junior engineers, resolve complex data issues, and manage team decisions for large-scale data migrations and deployments.

Build and maintain enterprise finance data products using Microsoft Fabric, Databricks, Azure Data Factory, Spark, and SQL Server. Develop Lakehouse bronze, silver, and gold layers, ingestion and transformation pipelines, incremental loading and CDC patterns, optimized Delta tables, semantic models, and AAS cubes. Ensure data quality, testing, monitoring, governance, data contracts, and CI/CD compliance while modernizing legacy SQL workloads. Collaborate with business stakeholders, analysts, and platform teams in enterprise or regulated environments.

Design and maintain automated data quality frameworks, validation controls, monitoring solutions, and reconciliation processes for large-scale financial datasets. The role identifies anomalies, missing or stale data, outliers, and reconciliation breaks; validates data from market-data providers; embeds controls into ETL/ELT pipelines; and supports onboarding of new investment datasets. Advanced Python, SQL, PySpark, financial data testing, profiling, root-cause analysis, and cloud data platform experience are required.

Design, build, and maintain scalable ETL pipelines, data warehouses, data lakes, and analytics platforms. Develop Streamlit dashboards, integrate PostHog behavioral data, create LangChain workflows, and use deepeval for model validation. Optimize SQL and database schemas, ensure data quality, governance, security, and privacy, monitor pipeline health, troubleshoot failures, and collaborate with data scientists and cross-functional teams.

Maintain Python-based web scraping configurations, monitor errors and data blockages, coordinate scraping requirements with stakeholders, and report on scraping activities. Build and optimize pipelines for data lake ingestion and transformation using ETL, data modeling, cloud, database, and infrastructure tools. Collaborate with data scientists and analysts while ensuring data security, compliance, testing, and maintainability.

Build and optimize large-scale ETL/ELT pipelines using AWS Glue, PySpark, Airflow or Step Functions, and Amazon Redshift. Manage high-concurrency, multi-terabyte data environments; support warehouse migrations; integrate APIs, databases, streaming inputs, and files; develop advanced SQL; and implement monitoring, governance, security, lineage, and cost optimization. Collaborate with technical and business stakeholders to deliver reliable, insights-ready datasets.

Build and maintain governed finance data products on Microsoft Fabric and Databricks. Responsibilities include developing PySpark and Python ingestion pipelines, implementing incremental loading and CDC, optimizing Delta Lake tables, managing Azure Data Factory pipelines and Analysis Services cubes, designing Power BI semantic models, and ensuring version control, testing, monitoring, governance, and data contract compliance. The role also involves stakeholder collaboration, mentoring junior engineers, and applying enterprise data architecture standards.

Build and optimize batch and real-time ETL/ELT pipelines using Python, PySpark, SQL, Snowflake, Databricks, and cloud platforms. Develop transformation and orchestration layers, implement data quality monitoring and alerting, optimize performance and cloud costs, and collaborate with architects, analysts, and data scientists. The role also requires data warehousing, dimensional modeling, CI/CD, version control, and documentation practices.

Build and support scalable batch and streaming data pipelines, cloud-native data platforms, data lakes, warehouses, and real-time processing systems. Own data initiatives from solution design through deployment, monitoring, performance tuning, and production support. Develop ETL/ELT pipelines, data models, and integration frameworks while collaborating with product, analytics, platform, and DevOps teams. Troubleshoot production issues, improve reliability, and contribute to documentation and engineering best practices.

Designs and supports batch and incremental data ingestion pipelines, ETL workflows, SQL transformations, and Airflow orchestration. Investigates data quality issues, reconciles discrepancies, performs data lineage analysis, supports cloud migration initiatives, and validates data across systems. The role also provides production support, resolves incidents, develops reusable engineering components, and collaborates with stakeholders on data requirements.

Designs and supports batch and incremental data ingestion pipelines, ETL workflows, SQL transformations, BigQuery data platforms, and Airflow orchestration. Investigates data quality issues, validates data across systems, performs lineage and source-to-target analysis, supports cloud migration initiatives, and resolves production incidents. Collaborates with stakeholders and engineering teams to define data requirements and develop reusable data engineering components.

Design and support batch and incremental data ingestion pipelines, ETL workflows, SQL transformations, and Airflow orchestration. Troubleshoot data quality, reconciliation, lineage, discrepancies, and production incidents. Support cloud data migrations, validate data across systems, collaborate with stakeholders, and develop reusable engineering components using BigQuery, SQL, Python, Airflow, and GCP services.

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.