Top Data Engineer Jobs in Mumbai

Senior Data Engineer role at Priceline within a hybrid work environment. The provided description focuses on company culture, benefits, inclusion, and application procedures, without detailing specific responsibilities, technologies, qualifications, salary, travel, or clearance requirements.

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.

Build and support fund accounting data solutions for asset management, including integrations, transformations, reconciliations, controls, and finance reporting products. Develop large-scale data pipelines using Databricks, Python, PySpark, and SQL; work with investment data, NAV calculations, accruals, corporate actions, fees, and general ledger information. Support cloud-based delivery, data quality, orchestration, CI/CD, and direct collaboration with US stakeholders.

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.

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.

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.

Build and optimize scalable real-time and batch ETL/ELT pipelines for manufacturing analytics using Azure Databricks, PySpark, and distributed computing. Design cloud data lakes, lakehouses, and warehouses; process IoT, MES, SCADA, LIMS, and ERP data; implement CI/CD, DevOps, Docker, and Kubernetes practices; monitor pipeline reliability; and provide clean data for AI/ML models. The role also involves data preprocessing, cloud optimization, troubleshooting, and collaboration with automation, engineering, and data science 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.

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.

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.

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.

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.

Designs and executes data, ETL, business intelligence, and database testing. Validates end-to-end data flows using SQL and BigQuery within GCP, performs data quality checks, documents defects, and supports root-cause analysis. Collaborates with developers, analysts, and stakeholders to improve test automation, delivery governance, and testing efficiency. The role requires 5–7 years of quality engineering or data testing experience, with preferred knowledge of cloud platforms, Python, dbt, and DevOps.

Lead the migration and modernization of enterprise Oracle databases to public cloud platforms. Administer Oracle 19c environments, Data Guard, ASM, backup and recovery, tuning, patching, and upgrades in a 24/7 setting. Develop Linux, shell, and Python automation; support cloud security, governance, lifecycle management, monitoring, and self-service provisioning. Collaborate with application teams, resolve incidents and alerts, and maintain operational documentation using Agile and DevOps practices.

Lead migration and modernization of enterprise SQL Server databases to public cloud platforms. Administer mission-critical SQL Server environments, including Availability Groups and Distributed Availability Groups, while managing tuning, capacity planning, backup and recovery, patching, upgrades, monitoring, and production support. Automate infrastructure and database operations with PowerShell and Terraform, maintain cloud security standards, resolve critical incidents, and collaborate with application stakeholders in an Agile environment.

Design, build, and maintain enterprise ETL/ELT pipelines in Microsoft Fabric; ingest and transform data from diverse sources into dimensional star schemas; optimize performance and cost; implement data quality checks and monitoring; document architecture and pipelines; collaborate with analysts, stakeholders, and global engineering teams to deliver Power BI-ready analytics datasets.

Design and build scalable data and AI infrastructure for production LLM applications. Responsibilities include developing RAG systems, embedding and vector retrieval pipelines, AI agents, LLM evaluation and observability frameworks, and batch or streaming data platforms using Databricks, Spark, Snowflake, Delta Lake, and Airflow. The role also involves optimizing distributed workloads, establishing data governance and quality practices, and partnering with engineering and product teams to productionize AI capabilities.

Develop and maintain Python-based API data ingestion services for GCP. Build secure, scalable, fault-tolerant pipelines with authentication, incremental loading, validation, reconciliation, orchestration, monitoring, and alerting. Manage data quality, PII masking, CI/CD, production troubleshooting, performance optimization, SLAs, data contracts, technical designs, mappings, and operational documentation. Collaborate with stakeholders to define support models and ensure reliable ingestion from internal and external APIs.

As an Azure Data Engineer, you will innovate and maintain data engineering services for Fortune clients, develop software for large projects, and utilize Azure components for solutions.