Top Data Engineer Jobs in Pune

As a Data Engineer, you will build scalable ETL/ELT pipelines, optimize data structures, and develop impactful Power BI solutions for stakeholders.

The Data Engineer will design and maintain data models, develop BI solutions, integrate data, and optimize data flows while collaborating with stakeholders.

Design and build a greenfield, multi-layer data warehouse and semantic metrics layer; separate analytics from OLTP; ensure data quality, traceability, tenant isolation, and high SLAs; enable linking of structured records to drone imagery/video; deliver self-service data access, observability, and infrastructure automation to scale across products and tenants.

Provide 24x7 L1 production support for wealth management applications, including incident triage, troubleshooting, application and batch monitoring, smoke testing, job scheduling, SQL-based data updates, alert management, escalation, root-cause analysis, reporting, disaster recovery documentation, and service improvement. The role coordinates with development, infrastructure, business, audit, compliance, and global support teams, with occasional night, weekend, and on-call duties.

Own the architecture, scalability, governance, and technical direction of Amtech’s cloud data platform, including warehouses, tenant data stores, feature stores, and event pipelines. Lead multi-tenant data architecture, hybrid AI data routing, data security, observability, and reliability standards. Build data foundations for analytics, machine learning, and AI agents, including vector databases and RAG pipelines. Provide technical leadership through design reviews, mentoring, roadmap planning, and cross-functional architecture collaboration.

Migrate SQL Server data products and analytical workloads to a modern cloud platform. Redesign legacy SQL, ETL processes, and business logic; build scalable pipelines and analytical models; validate data quality; collaborate with stakeholders; and improve platform reliability, performance, cost efficiency, and AI-assisted data engineering automation.

The role involves building and scaling the Acceldata Open Data Platform, focusing on observability, coding, debugging, and collaborating with teams and the open-source community.

Build and enhance enterprise data pipelines into Databricks Unity Catalog and Delta Lake. Improve remediation and entity-matching layers (including D&B DataBlocks), integrate APIs, and support LLM-assisted remediation and CMG redesign projects.

Design and implement distributed data systems for Apache Hudi and Onehouse’s managed data lakehouse. Responsibilities include developing concurrency controls, transactional capabilities, indexing schemes, metadata and data-access systems, and optimization algorithms for Kubernetes-based distributed processing. The role involves analyzing complex data, prototyping performance improvements, deploying solutions safely to production, and serving as a subject matter expert in databases, query engines, storage, and stream processing.

Build and productionize distributed data infrastructure for Onehouse’s managed lakehouse platform. Responsibilities include developing Apache Hudi transactional capabilities, concurrency control, indexing, metadata and data-access systems, and optimization algorithms for distributed processing on Kubernetes. The role involves analyzing complex data, prototyping performance improvements, collaborating with engineers, and safely deploying solutions. Candidates should bring strong object-oriented programming, distributed systems, algorithm, and relational database expertise.

Design, deploy, and maintain AWS and Databricks data infrastructure, data lakes, and scalable ETL pipelines. Build custom ingestion services and connectors using Airbyte, Go, and Python; optimize performance, reliability, monitoring, and data quality. Implement cloud data governance, security, access controls, encryption, and compliance practices while collaborating with platform engineers, analysts, and stakeholders.

Design, build, and maintain scalable Microsoft Fabric and Azure-based data platforms, ETL/ELT pipelines, lakehouse and data warehouse architectures. Implement orchestration, monitoring, data quality, CI/CD, and support analytics, AI, and production operations while following Specification-Driven Development practices.

Design, build, and maintain production data pipelines and models using Python and SQL. Improve data architectures (Kafka, warehouses, ODS), optimize SQL performance, perform data cleaning and analysis, select data sources, and collaborate with partners to support cloud-based analytics (Snowflake, AWS SageMaker) and workflow tools like Airflow.

The Data Engineer role involves designing and maintaining ETL pipelines, optimizing data storage solutions, collaborating with teams, and ensuring data accuracy and integrity.

Designs, builds, and maintains ETL/ELT pipelines feeding Microsoft Fabric Lakehouse platforms. Responsibilities include data ingestion, bronze/silver/gold layer development, CDC, incremental loading, data quality, metadata governance, performance tuning, monitoring, automation, and low-level design documentation. The role collaborates with architects, data modelers, and business analysts to deliver reliable, reusable datasets and promote self-service analytics while applying security, resilience, compliance, and cost-optimization standards.

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 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, 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%).

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.

Lead HUD's data quality strategy for RL training data: build QC systems, validate synthetic data at scale, design evaluation experiments, partner with researchers and vendors, convert research insights into production validation pipelines and tools, and mentor research engineers.

Design and operate governed, AI-ready data products, semantic layers, and natural-language analytics experiences on Snowflake and/or Databricks. Build ETL/ELT pipelines with dbt, Airflow, Snowpark, and PySpark; deploy Cortex or Genie capabilities; develop RAG applications; implement governance, security, lineage, and metadata controls; optimize platform and LLM costs; and partner with Finance stakeholders on trusted data products.

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 Python and Kubernetes Software Engineer, you will develop data analytics solutions using open source tools, collaborating with a distributed team.

Build, operate, and scale ingestion pipelines for marketing data (ad platforms, ecommerce, analytics). Normalize and transform data into shared schemas, ensure multi-tenant reliability, observability, and security, and partner with AI/data-science teams to expose clean data for agents.