Top Data Engineer Jobs in Pune

Lead and architect large-scale data engineering initiatives: design cloud-ready ELTL/real-time data platforms, drive AI adoption, ensure SLAs and quality across SDLC, mentor teams, and manage complex projects and vendor selections.

Design, build, and maintain scalable data pipelines and cloud data platforms to support AI/ML workloads. Deploy, monitor, and operationalize machine learning models, implement CI/CD and MLOps, ensure data quality, scalability, and governance while collaborating with data scientists, engineers, and stakeholders.

The Senior Software Engineer - Data Engineer will lead project phases, design technical solutions, manage team efforts, and collaborate globally while driving innovative data solutions.

Build and operate scalable batch and streaming data pipelines, implement transformations and analytics-ready data models, enforce data quality and observability, apply software engineering practices (CI/CD, testing), automate workflows, support production operations, and leverage AI to improve testing and pipeline reliability.

Designs, implements, automates, and maintains large-scale enterprise ETL processes for global clients. Acts as a data expert, delivers robust Test & Learn data solutions, enforces best practices (source control, code review, testing), and collaborates across global teams to meet project deadlines and quality standards.

Join the AI Innovation at Scale team to design and execute test strategies, perform white-box and black-box testing, validate data using Impala and Spark, monitor Spark job execution, collect logs, analyze quality data, and work with stakeholders to resolve issues and improve QA processes.

Build, test, and maintain GCP-based data pipelines and ETL/ELT processes using Python and SQL. Optimize BigQuery queries, design data models, ensure data quality, troubleshoot pipeline issues, document solutions, and collaborate with analysts and engineers to deliver scalable data warehouse solutions.

Lead design and delivery of cloud BI/analytics platform solutions using Databricks, Snowflake, AWS and Azure. Drive multi-cloud interoperability, governance, automation, security controls, and platform roadmaps to enable governed self-service analytics at scale.

Design, build, and maintain cloud-native data pipelines and ETL/ELT workflows for high-volume trade lifecycle data. Integrate DTCC sources (RTTM, Settlement Web) into a Unified Trade Record, process ISO 15022 messages, manage trade exception databases, optimize SQL Server performance, and ensure data quality, lineage, governance, and observability while collaborating with business stakeholders.

Lead design and build cloud-native batch and real-time data platforms. Develop scalable ETL/ELT and streaming pipelines (Spark, Kafka, Flink), ensure data quality, governance, observability, security, cost optimization, and drive platform engineering excellence and team mentorship.

Design, build, and maintain scalable data pipelines, warehouses, and lakes on cloud platforms. Implement real-time streaming (Kafka, Flink), parse payment message formats (ISO20022, SWIFT), transform data into AVRO schemas, integrate with IBM MQ/Kafka, collaborate with data scientists on ML model deployment, lead and coach teams, ensure security, testing, and operational controls.

Build, maintain and secure data pipelines, warehouses and lakes; design processing algorithms; collaborate with data scientists to deploy ML models; lead teams, manage risk and embed controls; partner with stakeholders to ensure accurate, accessible data.

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.

Design, develop and deliver scalable AWS-based data solutions and ETL pipelines using Python/PySpark, Snowflake, and AWS analytics services. Collaborate across teams, implement secure coding and unit testing, optimise data ingestion and transformations, and support project delivery and stakeholder engagement.

Design, build, and optimize scalable Snowflake data models and pipelines. Develop dbt transformations, orchestrate workflows with Airflow, write advanced SQL, ensure data quality and performance, collaborate with analytics teams, and maintain documentation.

Design, build, and optimize real-time stream processing pipelines using Flink and Kafka; ingest, transform, and enrich streaming data with Spark; integrate solutions with AWS services; monitor, troubleshoot, document, and enforce data governance and security.

Designs, builds and maintains data pipelines, warehouses and lakes; implements processing algorithms; collaborates with data scientists to deploy ML models; leads/supervises teams and ensures data quality, security and compliance.

Design, build, and maintain ETL/ELT data pipelines and data integrations. Optimize SQL queries and troubleshoot workflows, ensure data quality and governance, support data warehouse/lake implementations, work with cloud platforms, participate in code reviews, and document technical designs.

The AI Data Engineer will build and maintain data systems, design data architectures, collaborate with data scientists, and advise on strategies for data management and governance.

Design, build and maintain data pipelines, warehouses and lakes. Implement processing and analysis algorithms, ensure data security and quality, collaborate with data scientists to deploy ML models, and lead or mentor team members while partnering with stakeholders.

Design, build and maintain data pipelines, warehouses and data lakes on AWS. Develop processing and analysis algorithms, ensure data accuracy, security and governance. Collaborate with data scientists to deploy ML models. Lead or coach teams, advise on policy, mitigate risk and communicate complex technical information to stakeholders.

Design, build and maintain data pipelines, data warehouses and data lakes on AWS. Develop processing and analysis algorithms, collaborate with data scientists to deploy ML models, lead or support a team, ensure data security, accuracy and compliance, and drive continuous improvement.

Design, build and maintain data pipelines, warehouses and lakes on AWS. Ensure data quality, security and accessibility. Develop processing algorithms, collaborate with data scientists to deploy ML models, and lead/coach teams while managing risk and controls.

The Data Engineer will build and maintain data systems, pipelines, and warehouses, collaborating with data scientists and ensuring operational effectiveness.

Design, build, and maintain scalable AWS-based data enrichment pipelines (Spark) and orchestrations (Airflow); use S3/Glue/Athena for storage, processing and querying; perform deep data quality analysis, implement validation/observability, troubleshoot tracking and delivery issues across hybrid systems, and collaborate on governance, lineage, and schema design to support media measurement and audience intelligence.