Top Data Engineer Jobs in Mumbai

Owns Azure Databricks platform workstreams across data modeling, medallion architecture, Delta tables, data quality, Azure infrastructure, CI/CD, DevOps, security, and DataOps. The role involves designing and operating scalable production data platforms, managing Terraform and Azure configurations, implementing governance and PII protection, troubleshooting complex issues, and engaging stakeholders globally.

Designs and implements scalable, reliable data pipelines and platforms on AWS using Snowflake and dbt Cloud. Integrates data across warehouses, databases, lakes, and lakehouses to support analytics, reporting, predictions, and models. The role leads engineering quality, automation, testing, coding standards, technical debt management, stakeholder collaboration, and mentoring while delivering secure, high-performing data products in an Agile environment.

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.

Develop statistical and machine learning models to optimize Akamai’s global mapping service. Analyze massive telemetry streams, build predictive observability tools and automated data pipelines, define reliability metrics, investigate outages, and guide architecture and technology investment decisions. Collaborate with product engineers and communicate complex quantitative insights to technical and executive stakeholders.

Lead data engineering initiatives for CommBank’s HomeBuying Technology unit. Responsibilities include designing large-scale data pipelines and distributed systems, supporting cloud-native platforms, troubleshooting complex technical issues, applying DevSecOps and CI/CD practices, and leading high-performing engineering teams. The role also requires stakeholder management, coaching, agile delivery, operations support, and collaboration across banking technology teams.

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.

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 and maintain ETL pipelines for a large-scale analytics platform, ensure data quality, resolve data issues, handle support tickets, and assist senior engineers with enhancements. The role requires SQL and Python skills, basic cloud data platform knowledge, and familiarity with AWS Glue, Lambda, Redshift, and Apache Airflow. Experience in the Oil & Gas domain is preferred.

Extract and maintain support data, build pipelines and dashboards, analyze ticket trends and customer journeys, identify automation opportunities, and translate insights into prioritized improvements. The role implements Zendesk workflows and APIs, supports AI and automation initiatives, runs experiments, measures business impact, and communicates findings to technical and non-technical stakeholders.

Design and maintain scalable pipelines for visual and voice data, including ingestion, processing, validation, testing, observability, and automation. Build cloud, on-premise, and containerized data infrastructure; optimize storage, retrieval, and caching for large media assets; debug complex data issues; and collaborate with data scientists, operators, and product teams to support machine learning workflows.

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, 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 deliver cloud-native data platforms, scalable big data pipelines, lakehouses, warehouses, transformation frameworks, BI solutions, and ML-enabled data workflows. Lead client-facing pre-sales engagements, technical workshops, proof-of-concepts, executive conversations, and enterprise data modernization initiatives. Build reusable architectural IP, influence senior stakeholders, and translate ambiguous business challenges into production-ready solutions using AWS and modern data technologies.

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, 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, 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.

Develops, configures, and supports Informatica Product360 solutions for global product information management. Responsibilities include designing data models and workflows, building enterprise integrations, implementing data quality and governance controls, troubleshooting application and performance issues, supporting lifecycle activities, documenting processes, and participating in Agile delivery. The role collaborates with global stakeholders to improve product data quality and digital customer experiences.

Build and optimize ETL/ELT pipelines feeding BigQuery, audit and troubleshoot data quality, and create reliable Looker Studio dashboards. The role supports sales leaders by turning raw data into accurate, actionable insights, improving reporting performance, eliminating manual processes, and ensuring data integrity across Curvion’s revenue operations ecosystem.

Design and maintain analytical pipelines and reporting solutions using BigQuery and MariaDB. Develop Python and FastAPI tooling, APIs, and automation; write advanced SQL; apply AI/ML capabilities; document data architecture and lineage; and translate complex analysis into actionable business insights. Collaborate across departments on process improvements and independently own analytics projects end to end.

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.

Lead backend and data-platform architecture while remaining hands-on with Java production code. Design and operate distributed systems for event ingestion, analytical querying, data processing, and low-latency serving. Work with ClickHouse, BigQuery, Bigtable, Kafka, Redis, GCP, and Dataflow to solve scalability, reliability, performance, and data-correctness challenges. Set technical direction, review designs, mentor engineers, and guide platform convergence across Wingify and AB Tasty.

Develop robotics datasets and evaluations for embodied AI systems. Responsibilities include defining data schemas, annotations, quality standards, collection protocols, validation tools, and review workflows. The role also involves running experiments to assess how data quality and structure affect model performance, collaborating with research, engineering, vendors, and customers, and taking research tools from conception through deployment.

Develop a Java-based desktop IDE, coordination server, and relational data backend for a semiconductor test engineering DSL. Responsibilities include architecture, data modeling, versioning, synchronization, language editing and validation, debugging, automated testing, and platform quality. Collaborate with international engineering teams and domain experts to build a reliable, extensible development environment.

Develop a Java-based desktop IDE, coordination server, and relational data backend for a semiconductor test engineering DSL. Responsibilities include desktop UI development, data modeling, client-server synchronization, language editing and validation, architecture, debugging, automated testing, and platform quality. The role involves shaping a modular development environment and collaborating with international engineering teams and domain experts.