Top Data Engineer Jobs in Pune

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.

Design, build, and maintain scalable ETL/data pipelines using PySpark, Python, SQL and Azure Databricks. Ensure data quality and performance, troubleshoot and optimize workflows, collaborate with data scientists and product teams, and follow data governance and agile practices.

Lead design and deliver end-to-end data solutions on Microsoft Fabric. Build and optimize PySpark/ELT pipelines, architect Lakehouse/Delta Lake with Medallion patterns, enforce data governance/lineage/security, implement CI/CD and DevOps, mentor engineers, troubleshoot production, and collaborate with stakeholders on performance and cost optimization.

Design, build, optimize, and maintain high-performance Spark-based data pipelines using Scala/Java and Hive on Hadoop/CDP. Own full project lifecycle, enforce coding best practices, troubleshoot Spark/Hive/YARN performance, and collaborate with stakeholders to deliver scalable data solutions.

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.

Responsible for data management in Private Bank, aligning data requirements, ensuring data quality, and supporting data delivery through the group data hub.

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, deploy, test, commission, and maintain control system projects. Develop/modify design documentation, perform PLC/HMI/SCADA bench testing, support onsite and remote startups, coordinate installation QA/QC, and provide status updates to management and clients. Travel to client sites as needed (30-50%).

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 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.

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.

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.

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, maintain, and optimize ETL/ELT data pipelines and data warehouses. Troubleshoot production issues, write and optimize complex SQL, implement data security, monitor storage/processing, support cross-functional teams, and apply data science and AI/ML familiarity to data engineering solutions.

Responsible for building and operating data architectures, creating data pipelines, and implementing DataOps practices to support data scientists and engineers.

As a Senior Staff Engineer, you will design and optimize data pipelines, mentor engineers, ensure data quality, and collaborate with teams on data integration.

Senior Data Engineer responsible for designing and tuning complex SQL, building ETL/ELT pipelines, querying data lakes with Spark SQL/Trino/Presto, implementing materialized views, partitioning and vacuuming, and developing ACID-compliant Delta Lake tables in Parquet. Requires deep transactional, normalization/denormalization, and query optimization expertise and 10+ years experience with effective English communication.

Design, build, and operate scalable Snowflake-based data platforms and ELT pipelines ingesting from relational, NoSQL, and streaming sources. Own Snowflake administration, cost governance, security, data modelling, observability, and Trino analytics layer. Collaborate with product and business teams to deliver tested, documented datasets and ensure compliance with PII and regulatory requirements.

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.