Top Data Engineer Jobs in Mumbai

Design, build, and operate scalable ETL/ELT pipelines using open-source tools. Optimize databases and SQL, prototype new tools, support migration from Azure Databricks to AWS, ensure data quality, observability, CI/CD, and collaborate across distributed teams in Advertising and Market Research.

Design, develop, and optimize scalable cloud-native AWS data platforms and high-performance batch/analytical pipelines using Spark/PySpark and Apache Iceberg. Implement Lakehouse solutions, optimize query and storage performance, ensure observability, data quality, and production support, and follow CI/CD and data engineering best practices while collaborating with stakeholders.

Build and maintain scalable batch and real-time data pipelines using Kafka, Maxwell, Spark and dbt; design medallion-based data models; implement cloud-native data platforms (S3, Trino, BigQuery); optimize performance, ensure data quality, observability, and SLA adherence; enable analytics through trusted datasets and Metabase dashboards.

The Senior Data Scientist & Engineer will build and manage data infrastructure, analyze product usage, build models, track metrics, run experiments, and communicate insights to guide product decisions.

Senior technical leader responsible for designing, building, and scaling cloud-native data platforms and pipelines. Partners with stakeholders, mentors engineers, enforces engineering best practices, ensures data modeling, governance, and quality, debugs production issues, and contributes to architecture, design, and Agile delivery to support business outcomes.

This posting is a registration/marketing notice for Job Dials recruitment portal asking candidates to register on the site and follow social links. It provides company background and registration links but lists no specific Data Visualization Engineer responsibilities, qualifications, salary, or application details.

Design, build, and productionize end-to-end cloud data solutions and pipelines. Translate business needs into data architectures, ensure performance, security, reliability, and scalability, lead technical reviews and client workshops, produce documentation and runbooks, mentor peers, and contribute reusable patterns, accelerators, and best practices for cloud data platforms.

Design, build, and maintain scalable Java Spring Boot backend microservices integrated with blockchain/Web3 infrastructure. Develop REST APIs, event-driven systems (Kafka/JMS), SQL/NoSQL data stores, and containerized cloud deployments (Kubernetes). Ensure performance, security, and reliability while collaborating with cross-functional teams in Agile environments.

Design, build, and maintain scalable cloud-native batch and streaming data platforms and pipelines. Own end-to-end solutioning, deployment, production support, performance tuning, monitoring, and mentorship while collaborating with product, analytics, and infrastructure teams.

Design, build, and operate scalable batch and real-time data pipelines and analytics platforms. Partner with enterprise customers to define requirements, own technical delivery from discovery to adoption, and present architectures to stakeholders. Drive data governance, observability, CI/CD, and platform optimization while mentoring junior engineers and collaborating with Product, Engineering, and Customer Success teams to enable analytics and AI/ML applications.

Design, build, and optimize large-scale ETL pipelines and distributed data applications (batch and streaming) using Spark and Scala. Ensure data quality and performance, deploy and manage workloads in cloud/container environments, troubleshoot production issues, and collaborate with product, engineering, and analytics teams to deliver scalable AdTech data solutions.

Design, build, and maintain scalable ETL/ELT pipelines and cloud data warehouse transformations. Create dimensional schemas and semantic models, enforce automated data validation, optimize query performance and partitioning, manage backend data layers for BI, and collaborate with business and engineering teams to translate requirements into robust analytics architectures.

Design, build, and operate scalable data engineering services and ingestion pipelines (CDC/streaming). Develop and optimize a centralized analytics warehouse, data models, and aggregation strategies. Build APIs, enforce multi-tenant security and data governance, monitor pipeline health, improve automation and observability, and ensure production reliability through strong engineering practices.

Design and implement AI/LLM-driven data extraction and ETL pipelines, build web crawling and API integrations, implement RAG systems, mitigate LLM hallucinations, evaluate third-party tools, and collaborate with content and analytics teams to deliver high-quality, scalable data for downstream analysis.

The Big Data Engineer will support existing applications and work on new design and implementation requirements, utilizing big data technologies and tools.

The role involves data analytics with a focus on querying large data sets, visualization using Tableau, and applying quantitative skills, predominantly in the FMCG and Food & Distribution sectors.

The Data Analyst prepares, analyzes, and validates data, applying statistical methods and visualization techniques to derive insights and support business objectives. Communicates findings effectively and collaborates with Data Engineers for model and dashboard implementation.

Triage and resolve Master Data Management (Party, Risk, Reference) issues in Stibo STEP, run targeted data-cleansing sprints, monitor data quality dashboards, support releases/UAT, and maintain documentation and playbooks to prevent recurring data issues.

Design and implement data federation and lakehouse architectures (Starburst/Trino), build scalable ETL/ELT pipelines using Python and Spark, optimize Spark and federated queries, manage Delta/Iceberg/Hudi tables, and enforce governance, access control, and data masking for analytics and AI use.

As a Managed Services Engineer (L2), you will maintain client IT infrastructure, resolve incidents, manage tickets, and support automation efforts while ensuring client satisfaction.

Lead and mentor a data engineering team to design, build, and optimize scalable batch and real-time data pipelines using Databricks, Spark, Delta Lake and cloud services (Azure/AWS). Implement Medallion architecture, ETL/ELT workflows (ADF/Databricks Workflows), CI/CD and IaC, ensure data governance, performance, and stakeholder collaboration.

Design, build, and maintain cloud data architectures and automated ETL pipelines (Airflow/Talend). Optimize platform performance using Python, Docker, and Kubernetes. Collaborate with global teams to support reporting, integration, and analytics in an Agile/Scrum environment and evaluate emerging data technologies.

Design and build scalable ELT pipelines and data models using Databricks, dbt, Airflow and BI tools. Lead data modeling strategy, implement observability and governance, collaborate with data scientists and product teams, mentor peers, and deliver self-serve analytics and data products that drive business outcomes.

Design, build, and operate cloud-native data platforms and reliable ETL/ELT pipelines (batch and streaming) using Databricks and Spark on AWS/Azure. Ensure production readiness—security, testing, monitoring, cost efficiency—and optimise performance, reliability, and data quality. Collaborate with analysts, data scientists and platform teams, mentor junior engineers, support production troubleshooting, and contribute to technical design and operational standards.

The Senior Data Engineer will design and build data infrastructure and pipelines, enhance AI capabilities, and collaborate with teams on data solutions while mentoring junior engineers.