Top Data Engineer Jobs in Mumbai

Develops business intelligence solutions, reports, dashboards, and data visualizations using Power BI, Tableau, SQL, and ETL tools. Partners with stakeholders to gather requirements, design solutions, deliver analytics products, and provide training. Analyzes trends, anomalies, and business opportunities while applying AI-enabled analytics and development tools to improve reporting, automation, documentation, testing, and delivery efficiency.

Build and maintain scalable data solutions, including automated ETL and ELT pipelines, data models, integrations, and cloud data platforms. Gather requirements, create technical designs and documentation, collaborate with stakeholders, estimate and deliver work, support platform roadmaps, and promote data engineering best practices. The role requires commercial data engineering experience, cloud platform expertise, SQL proficiency, and strong communication skills.

Build and maintain FinOps data pipelines, BigQuery and dbt models, cost attribution systems, forecasting and anomaly detection models, and cost-versus-revenue analytics. Identify and implement AWS and GCP savings opportunities, automate governance with Python, Terraform, and Lambda, and partner with Engineering, Finance, Product, and leadership on cloud costs, forecasting, reporting, and architecture decisions.

Designs and develops scalable data architectures, cloud data solutions, data warehouses, business analytics tools, and complex data pipelines. The role involves data wrangling, exploration, and discovery across heterogeneous sources, using AWS, SQL, Python, and relational database technologies. The engineer also contributes to data engineering innovation, knowledge sharing, and project planning while enabling data-driven organizational decision-making.

Designs, builds, and maintains scalable data infrastructure, architectures, data models, ETL pipelines, and workflow orchestration systems. Ensures data quality through validation, error handling, and logging frameworks. Uses Apache Airflow, Tidal, Control-M, Git, CI/CD, Databricks, PySpark, and Spark SQL to support reliable data movement, large-scale processing, analytics, and business intelligence initiatives.

Designs and maintains scalable data pipelines and ETL/ELT workflows using Python, SQL, and Azure Databricks. Manages large structured and semi-structured datasets, data models, monitoring, quality controls, troubleshooting, and production support. Integrates LLM and Generative AI capabilities, including APIs, embeddings, prompt engineering, and RAG workflows, into data solutions. Collaborates with engineering, analytics, and business teams while following security, CI/CD, documentation, and maintainability best practices.

Design, build, and optimize GCP data ecosystems, including pipelines, data lakes, warehouses, storage architectures, and processing infrastructure. Gather requirements, model data, troubleshoot complex issues, tune performance, monitor systems, and support new functionality. Collaborate directly with clients and cross-functional teams, while mentoring junior data engineers and leading technical improvement discussions.

Build and own end-to-end Microsoft Fabric solutions across data integration, lakehouse and warehouse architectures, semantic models, Power BI reporting, AI-enabled analytics, governance, security, testing, deployment, and production support. Develop reusable pipelines, notebooks, dimensional models, and automated deployments while optimizing performance and cost. Collaborate in agile delivery, resolve cross-component issues, document solutions, and mentor junior engineers.

Provides technical leadership for Azure data engineering projects, including solution architecture, data modeling, pipelines, data integration, code quality, QA, performance optimization, governance, security, and documentation. Collaborates with stakeholders and project managers to define scope, manage risks, and deliver scalable, efficient solutions. Requires strong Python, SQL, PySpark, Databricks, Azure Data Factory, Delta Lake, Synapse, Spark, data warehousing, governance, and Azure DevOps expertise.

Leads the design, development, and maintenance of scalable ETL/ELT pipelines and data products across distributed, multi-cloud environments. Defines data engineering architectures, resolves complex production issues, and designs efficient data solutions. Provides technical leadership and mentorship while collaborating with architects, engineers, and business stakeholders. Requires expertise in big data, AWS services, data warehousing, databases, data modeling, orchestration, streaming, security, observability, version control, containers, and modern development practices.

Develop and maintain data pipelines, models, database structures, and orchestrations on Microsoft Azure. Transform vessel, project, campaign, and third-party data into high-quality datasets and reports. Collaborate with business stakeholders to refine requirements, support data scientists and report developers, and design modern cloud data solutions. The role requires strong data modeling, ER modeling, SQL or programming experience, and at least five years of relevant experience.

Develop and maintain Oracle APEX applications, Oracle Analytics Server/OBIEE reporting solutions, BI Publisher reports, and Oracle database objects. Build dashboards, workflows, integrations, REST APIs, and data models; write complex SQL and PL/SQL; troubleshoot and tune application and database performance; support deployments, testing, production issues, documentation, and stakeholder collaboration.

Leads cloud and on-premises database platform administration, migration, refactoring, automation, and support. Manages SQL Server, PostgreSQL, MySQL, and MongoDB environments; resolves performance issues, failures, and alerts; implements high availability, disaster recovery, security, and clustering solutions. Develops migration tooling and documentation, supports CI/CD and infrastructure-as-code practices, and collaborates with application and cross-functional teams in a 24/7 environment.

Develop and maintain SAP ABAP solutions, customizations, reports, workflows, and integrations. Build SAP data extraction and integration pipelines using ODP, CDS Views, OData, and SAP Datasphere. Translate business requirements, especially SAP SD processes, into scalable technical solutions and integrate SAP data with cloud platforms. Collaborate with functional consultants, data engineers, stakeholders, and external partners while independently resolving technical issues and strengthening internal SAP capabilities.

Build and lead end-to-end data-centric solutions for Business Banking. Responsibilities include defining ETL strategies, developing pipelines, designing scalable data solutions, collaborating with stakeholders and infrastructure teams, ensuring secure and highly available delivery, contributing to agile planning, and mentoring engineers. The role requires extensive Snowflake, cloud, ETL, Hadoop, database, BI, and Python experience, alongside strong analytical and communication skills.

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.

Designs and maintains scalable data ingestion, transformation, integration, and ETL/ELT pipelines. Diagnoses data quality issues, performs root-cause analysis, implements sustainable fixes, and optimizes SQL and data-processing workloads. Integrates internal and external systems through APIs and batch interfaces, supports CI/CD and DevOps practices, and ensures data accuracy, governance, security, and compliance. Collaborates with architects, analysts, QA teams, and stakeholders to deliver reliable data for analytics, reporting, and downstream applications.

Develop and support Laserfiche document-digitization and workflow solutions, including custom C#/.NET and VB/VB.NET components, SQL Server integrations, OCR, metadata processing, automated ingestion, and repository services. Troubleshoot production workflows and Windows services, implement monitoring, testing, deployment controls, and documentation, and integrate Microsoft 365 and Graph APIs. Collaborate with infrastructure, DevOps, security, QA, and application teams while providing technical leadership, code reviews, mentoring, and knowledge transfer.

Designs and maintains Salesforce Data Cloud solutions for audience segmentation, activation, real-time ingestion, and data governance. Responsibilities include developing SQL queries, configuring Data Extensions, Data Model Objects, Data Streams, Calculated Insights, and Computed Traits; architecting cloud integrations with Snowflake, AWS, Azure, or GCP; resolving data quality issues; and implementing consent and governance frameworks.

Leads enterprise architecture strategy for GMPD Technology, aligning business objectives with scalable business, application, data, and technology architectures. Develops roadmaps, standards, governance, and reference architectures; guides solution delivery and evaluates risk, ROI, vendors, and emerging technologies. Partners with technical and business stakeholders, supports cloud and data architecture initiatives, communicates with senior leadership, and promotes architecture practices across complex transformation programs.

Designs and maintains scalable data pipelines using AWS, Spark, Kafka, Airflow, and related technologies. Collaborates with product, data science, and business teams; develops reporting dashboards; documents data flows and runbooks; optimizes workflows; troubleshoots data processing issues; and supports Agile delivery and CI/CD practices.

Develop and optimize Supabase’s Data API based on PostgREST. Responsibilities include improving the PostgreSQL driver and performance, developing features, fixing bugs, resolving incidents and escalated support issues, improving observability, and debugging integrations across the Supabase platform. The role requires strong Haskell development, PostgreSQL and SQL expertise, Linux debugging, open-source contribution, and technical user support.

Build and evolve a reliable, scalable data platform supporting analytics, credit models, experimentation, and operational reporting. Responsibilities include creating batch and near-real-time ingestion frameworks, data modeling and semantic layers, governance and security controls, lineage, PII handling, observability, and platform roadmaps. The role also involves scaling backend services, partnering with Product and Data Science teams, and supporting large distributed datasets using modern orchestration, warehouse, and programming technologies.

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 and evolve distributed data architectures, infrastructure platforms, and database systems at massive scale. Lead initiatives involving sharding, replication, migrations, reliability, performance, scalability, and infrastructure cost efficiency. Influence database technology choices and long-term technical strategy across engineering teams. Build reusable infrastructure capabilities, solve systemic technical problems, and lead through technical influence without formal authority.