Top Data Engineer Jobs in Mumbai

Looking for a Data Engineer lead to design and build scalable solutions, develop data pipelines, and support analytics and ML initiatives, while collaborating with cross-functional teams for quality data management.

Architect and optimize enterprise Power BI and Microsoft Fabric reporting solutions, semantic models, data pipelines, and governance. Develop interactive reports, integrate Fabric AI Agents, and enable self-service analytics. Manage Git-based CI/CD deployments across environments, enforce row- and object-level security, and automate data quality validation using Python, SemPy, PySpark, and Spark SQL. Build scalable data transformations and automate alerts for data anomalies.

Build and deploy Snowflake data warehouse solutions, develop Python-based ETL and ELT pipelines, design dimensional and star schemas, support data strategy and governance, and integrate operational data for analytics and reporting. Collaborate across development teams, participate in code reviews, troubleshoot complex data issues, and contribute within an Agile/Scrum environment.

Leads data engineering initiatives involving ETL/ELT pipelines, database optimization, orchestration, big data technologies, data modeling, BI performance, and data quality monitoring. Requires strong Scala, Python, SQL, and shell scripting skills, plus payments-domain expertise covering transaction processing, settlements, disputes, reconciliation, gateways, acquiring, issuing, and PCI-DSS compliance. The role also involves cross-functional communication, planning, facilitation, negotiation, and collaboration with senior stakeholders and clients.

Design, build, and optimize end-to-end data solutions using Microsoft Fabric, Azure Synapse, Databricks, Azure Data Factory, and Power BI. Develop scalable real-time and batch pipelines, data models, semantic and dimensional models, dashboards, reports, and self-service analytics. Apply medallion architecture, DataOps, monitoring, security, governance, quality controls, and performance tuning. Integrate Azure Machine Learning and streaming technologies when needed while delivering enterprise data and BI solutions for clients.

Lead and hands-on build cloud-native, microservice-based systems for the PitchBook platform. Drive architecture and design, implement testable scalable code, define non-functional requirements, mentor engineers, collaborate with architects and stakeholders, and deliver end-to-end initiatives while promoting engineering best practices and continual improvement.

Build and manage data connections, validate and normalize incoming portfolio files, automate monitoring and reporting, improve auto-reconciliation via data mining and transformations, support operations and development requests, and communicate technical solutions to non-technical stakeholders. Participate in light on-call duties.

Design, build, and maintain data connections and transformations, validate file deliveries, normalize external datasets, automate monitoring/reporting, improve auto-reconciliation, support operations and development, and communicate technical details to stakeholders.

Designs and maintains SAP-integrated ETL/ELT pipelines connecting ECC, S/4HANA, BW/4HANA, and cloud data platforms. Responsibilities include SAP data extraction, transformation, real-time integrations, dimensional modeling, database optimization, data quality monitoring, alerting, cost optimization, and documentation. The role partners with functional consultants, BI teams, FP&A stakeholders, and business leaders while developing cloud skills across AWS services such as Glue, Lambda, Airflow, and Redshift.

The Senior Azure Data Engineer will design, develop, and maintain data infrastructure and pipelines, ensuring data quality, integrity, and alignment with business objectives. Responsibilities include collaborating with teams and establishing best practices for data engineering.

Own the end-to-end real-time voice and data platforms, including AI agents for live calls, telephony and streaming integrations, call-data pipelines, analytics, dashboards, monitoring, and cloud infrastructure. Build AI workflows, product interfaces, and business automations; optimize latency, cost, reliability, and performance; respond to production issues; and mentor teammates while collaborating with product and domain experts.

Design, build, and maintain Azure-based data infrastructure and pipelines. Implement data models and schemas, ensure data quality, optimize distributed storage and processing (Databricks/PySpark), and establish engineering best practices while partnering with cross-functional teams.

Design cloud-native data and AI architectures, including data lakes, warehouses, ETL/ELT pipelines, real-time streaming systems, and infrastructure as code. Review architectural patterns, optimize performance and costs, align delivery with business objectives, support client presentations and workshops, and mentor junior team members. The role requires cloud solution design experience across AWS or Azure, knowledge of data and AI patterns, AI algorithm evaluation, communication skills, and an AWS Certified Solutions Architect Professional certification.

Designs cloud-native data and AI architectures, including data lakes, warehouses, ETL/ELT pipelines, real-time streaming, and infrastructure as code. The role evaluates cloud storage and processing options, optimizes performance and costs, guides architectural development, supports client workshops, aligns technical delivery with business objectives, and mentors junior team members. AWS or Azure expertise, AI algorithm experience, and AWS Certified Solutions Architect Professional certification are required.

Design and implement cloud-native data lakes, warehouses, AI architectures, ETL/ELT pipelines, and real-time data systems. Guide architectural patterns, optimize performance and costs, align technical delivery with business goals, support client workshops, and mentor junior team members. The role requires expertise across major cloud platforms, data engineering tools, infrastructure as code, AI algorithm evaluation, and cloud solution architecture.

Designs, develops, and maintains scalable data architectures, ETL pipelines, data lakes, warehouses, data models, and data catalogs. Builds data solutions using Azure Data Factory, Azure Data Lake, Databricks, Scala, .NET, Python, and PySpark. Partners with data scientists, business stakeholders, analysts, and architects to deliver analytics-ready data products, establish governance and standards, mentor professionals, evaluate emerging tools, and support strategic enterprise initiatives.

Develop data-driven solutions by translating business problems into data problems, collecting and preparing data, building and evaluating statistical and mathematical models, and deploying models into applications. Manage data security, privacy, quality, availability, governance, migration, and policy. Operate and troubleshoot ELK environments, distributed clusters, Linux systems, and cloud infrastructure while communicating technical insights to diverse stakeholders.

Build and lead scalable batch and real-time data pipelines on AWS for security telemetry, analytics, detection, and AI/ML use cases. Develop Python frameworks, data models, and data products while ensuring quality, reliability, observability, and security. Collaborate with data scientists and engineers to productionize models, integrate enterprise security platforms, and implement DevOps, CI/CD, automation, infrastructure-as-code, and encryption practices. Own technical strategy and the end-to-end data engineering lifecycle.

Design and develop scalable data-driven solutions and pipelines using Python and SQL. Leverage Azure data services to build secure, high-performance architectures, ensuring data integrity, governance, and overall success of data projects.

Design and build scalable data pipelines and architectures on Azure using ADF, Databricks, and Synapse. Implement Kimball/Data Vault modeling, write ETL in Python and T-SQL, ensure data governance (Azure Purview), automate tests, troubleshoot issues, document processes, and collaborate across teams.

Design and develop data-driven solutions and scalable data pipelines using Python and SQL. Leverage Azure data products to build robust, secure architectures, ensuring data integrity, performance, and governance while supporting data-centric client projects.

Designs, develops, and governs enterprise data platforms using Databricks and Microsoft Fabric. Builds scalable pipelines, lakehouses, warehouses, semantic models, data marts, automation frameworks, and data-quality validation systems. Leads data engineering through the full delivery lifecycle, implements security and governance controls, supports CI/CD and infrastructure automation, optimizes Spark workloads, and collaborates with stakeholders and technical teams. The role also includes mentoring engineers and establishing reusable engineering standards.

Design, build, and support cloud-based data pipelines, data models, and enterprise data warehouse solutions. Partner with stakeholders on business initiatives, troubleshoot data and performance issues, lead projects, mentor developers, evaluate technologies, and drive automation and continuous improvement using GCP and related data engineering tools.

Designs, builds, and supports cloud-based data pipelines, data models, warehouses, and integrations, primarily using GCP. Troubleshoots performance and technical issues, leads enterprise initiatives, mentors developers, evaluates data technologies, and drives automation and continuous improvement. The role partners with business analysts, architects, and stakeholders to deliver global data engineering projects.

Lead the migration and modernization of enterprise SQL Server databases to public cloud platforms. Administer 24/7 SQL Server environments, implement high-availability and disaster-recovery solutions, tune performance, manage backups, patching, and upgrades, and automate infrastructure with PowerShell and Terraform. Support cloud security, provisioning, monitoring, incident resolution, and application stakeholder collaboration within Agile and DevOps environments.