Top Data Engineer Jobs in Mumbai

Build and maintain Databricks-based data integration and ETL pipelines using Python, PySpark, DLT, Delta Lake, and Unity Catalog. Design governed semantic models and self-service analytics layers, support data governance and access controls, and deliver executive dashboards in Tableau or Sigma. Partner with business stakeholders to translate requirements into actionable insights while maintaining production-quality code, documentation, lineage, and compliance in a regulated environment.

Design and build data services and pipelines supporting machine learning products. Develop automation tools for model deployment, maintain production data systems, improve infrastructure, participate in code reviews, and collaborate across engineering, data science, and product teams. The role requires expertise in distributed systems, large-scale data processing, CI/CD, container orchestration, and AI-enabled workflow improvements.

Design, optimize, and productionize scalable ETL/ELT pipelines using Databricks, PySpark, SQL, GCP, BigQuery, and Delta Lake. Build agentic workflows for data validation, troubleshooting, and automation. Develop reusable data engineering frameworks, implement quality and monitoring controls, optimize workloads, support deployments, and resolve complex production issues while collaborating with business, platform, and engineering teams.

Designs, develops, and supports enterprise data platforms using Python, SQL, Airflow, dbt, Snowflake, cloud technologies, and modern data architectures. Responsibilities include building scalable pipelines and reusable frameworks, implementing metadata, lineage, governance, quality, and observability capabilities, integrating Agentic AI, supporting production operations, improving performance and reliability, leading technology evaluations, and mentoring engineers. The role also contributes to Agile delivery, testing, deployments, documentation, demonstrations, and cross-functional platform modernization initiatives.

Designs, develops, and maintains data foundations, pipelines, ETL/ELT processes, and data integration solutions. Ensures data quality, supports migration across systems, and translates data strategy into scalable technology solutions. Collaborates with technical and business stakeholders, presents findings, leads implementation initiatives, manages change and incidents, and mentors junior engineers. Required expertise includes Azure, Databricks, data modeling, cloud data platforms, orchestration, streaming, and SQL technologies.

Designs, develops, tests, deploys, and maintains data pipelines, ETL processes, data warehouses, and data models supporting business intelligence reporting. Configures automation, validates data quality, monitors performance and errors, troubleshoots issues, and optimizes ETL environments. Collaborates with technical users and business stakeholders to define requirements, establish business rules, and deliver accurate datasets and analytics solutions.

Support the assessment, validation, governance, and monitoring of AI/ML and Generative AI solutions used in cybersecurity. Evaluate model changes and outputs, design guardrails and thresholds, document validation results, and ensure alignment with regulatory, risk, responsible AI, and enterprise security standards. Collaborate with technology, risk, business, and vendor stakeholders to improve model effectiveness, explainability, and operational resilience.

Design, build, and optimize scalable data pipelines and event-driven architectures on GCP. Develop Python applications, integrate external APIs, manage BigQuery and cloud storage solutions, and automate deployments with Cloud Build. Ensure pipeline performance, scalability, cost efficiency, data quality, governance, lineage, and security while collaborating with cross-functional teams and documenting technical solutions.

Design, build, and optimize scalable ETL/ELT pipelines and event-driven data architectures on GCP. Develop Python applications, integrate external APIs, deploy cloud services, automate CI/CD with Cloud Build, and manage data quality, governance, lineage, security, performance, and cost efficiency. Collaborate with cross-functional teams to deliver scalable data solutions and maintain technical documentation.

Own the architecture, technical direction, reliability, cost efficiency, and adoption of Atlassian’s analytical data platform. Build foundational ingestion, transformation, orchestration, compute, and consumption components; establish multi-tenant standards and SLOs; and lead cross-team technical initiatives. The role requires hands-on development, distributed systems expertise, batch and streaming architecture design, cloud experience, and mentorship of senior engineers.

Build and optimize scalable data pipelines, ingest structured and streaming data, develop Redshift data models, optimize SQL, and support AWS cloud migration. The role uses Airflow, dbt, Kafka, Python, and AWS services while emphasizing data quality, observability, security, governance, and reliability in a mission-critical payments environment. Collaboration with global product, engineering, analytics, and business teams is central.

Build Gather AI’s data foundation from scratch by creating PostgreSQL extraction pipelines, dbt models, semantic metrics, data quality controls, tenant isolation, lineage, and documentation. The role includes warehouse data movement, ingestion-boundary validation, CI/CD delivery, safe backfills, on-call ownership, and collaboration across product, ML, security, and integration teams. Engineers will initially support the Drone product and later expand the platform to additional robotics and warehouse products.

Build and optimize enterprise data pipelines and integrations using Snowflake, SAP HANA, dbt, SQL, and Python. Design production-ready generative AI, LLM, RAG, and AI Agent solutions; manage Control-M workflows and system integrations; and support platform modernization, data quality, automation, governance, and enterprise AI adoption. Collaborate with architects, engineers, and business stakeholders to deliver scalable, secure solutions from design through production.

Designs, develops, and supports scalable Azure-based data platforms, ETL/ELT pipelines, SQL Server databases, Python integrations, REST API connections, and automation solutions. The role includes troubleshooting production issues, improving data quality and performance, documenting systems, gathering requirements, and collaborating with international stakeholders. The engineer may also support Azure AI integrations and serverless solutions while contributing to architecture and platform improvements.

Designs, builds, and optimizes scalable data platforms, distributed systems, and high-volume ETL pipelines supporting trading and business operations. Develops solutions using Java, Python, Kafka, microservices, and modern databases while ensuring reliability, governance, data quality, and performance. Automates operational processes, improves application scalability, collaborates with trading and technology teams, and mentors junior engineers.

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.

Leads cybersecurity data-source onboarding and integration for Zscaler’s security platform. Builds and automates Python/API-based data transformations, maps and normalizes security data, manages pipeline lifecycle activities, troubleshoots data quality, and identifies security gaps. Partners with cybersecurity SMEs and cross-functional teams to create implementation plans, communicate technical findings, and provide product improvement feedback. The role requires expertise in security platforms, diverse security-tool integrations, data modeling, SQL, and Python.

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.

Develop AI-powered product features for local discovery, including personalized recommendations, content curation, search, and ranking. Build data pipelines by scraping, cleaning, and structuring datasets for machine learning models. Collaborate with product, design, and engineering teams to integrate AI into the platform, while continuously improving machine learning capabilities and user experiences.

Build and optimize Snowflake and dbt data pipelines with SQL and Python. Design enterprise data engineering and AI solutions involving LLMs, RAG, and AI Agents; manage system integrations and Control-M workflows; and deliver secure, scalable production solutions. Collaborate with technical and business stakeholders on platform modernization, data quality, automation, responsible AI, governance, and enterprise adoption.

Designs, builds, and maintains scalable ETL pipelines and data products across multi-cloud, multi-region environments. Investigates and resolves technical, procedural, and operational data issues while applying disciplined development practices. The role requires expertise in data engineering patterns, architecture, big data, programming, warehousing, orchestration, databases, data modeling, streaming, security, version control, containerization, monitoring, and logging.

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 optimizes scalable data architectures, ingestion pipelines, ELT/ETL transformations, cloud data warehouses, and orchestration workflows. The role uses Python, SQL, dbt, Spark, and platforms such as Snowflake, Databricks, and BigQuery. Responsibilities include data quality validation, query and cost optimization, dimensional modeling, access controls, data masking, compliance, and enterprise data warehouse migrations. Familiarity with streaming technologies and mandatory certification in Snowflake, Databricks, or Google Cloud is required.

Build scalable data pipelines and ETL/ELT processes using modern data platforms. Perform exploratory analysis, validate data, develop models supporting analytics and real-time insights, and collaborate with data science, product, engineering, and customer success teams. Write tested Python code, contribute to data quality initiatives, and deliver end-to-end features in an agile environment.

Build scalable data pipelines and ETL/ELT processes using Python and modern data platforms. Perform exploratory analysis, validate data, develop data models, and support analytics and real-time insights. Collaborate with data scientists, analysts, engineers, and product teams while delivering tested, reliable features. The role also contributes to data quality policies, processes, scorecards, and product operations reporting.