Top Data Engineer Jobs in Pune

Lead the design and development of scalable cloud-native data platforms and batch or real-time pipelines supporting AI, machine learning, and Generative AI. Build infrastructure for feature engineering, model training, inference, LLMs, RAG, embeddings, and vector databases. Ensure data quality, governance, security, observability, reliability, and cost efficiency while enabling MLOps and AgenticOps capabilities across enterprise AI initiatives.

Supports the design, implementation, and maintenance of enterprise ETL processes and data pipelines for global client engagements. Uses SQL and database technologies to optimize processing and develop scalable code for large datasets. Collaborates with engineers, analysts, clients, and global teams; applies version control, code reviews, data validation, testing, and process automation. Troubleshoots data issues, supports multiple projects, and ensures accurate, compliant, high-quality data solutions.

Develop and configure ZAIDYN data pipelines and analytics-ready and AI-ready data products. Responsibilities include ETL development, data modeling, data quality rules, KPI computation, cloud and big-data integration, testing, deployment, documentation, and client delivery. The role translates business requirements into technical solutions, collaborates with global engineering and analytics teams, supports project planning, mentors junior colleagues, and manages assigned workstreams.

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

Design and implement ETL/data automation using Alteryx and SQL, build Tableau dashboards, develop Power Platform solutions, manage Alteryx Server, gather requirements, deliver end-to-end analytics, and continuously improve reporting and data integration.

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.

Design and implement scalable end-to-end data pipelines and storage schemas across cloud and on-prem platforms. Define reference architectures and engineering standards, ensure governance/security/compliance by design, optimize performance for large datasets, and mentor/lead global teams using Python/Java, SQL, Spark, Databricks, and related data platforms.

Design, build, and scale cloud-native data and BI solutions on Databricks and Snowflake. Develop high-performance data pipelines, enable multi-cloud interoperability (AWS/Azure), implement governance and secure access patterns, maintain CI/CD and infrastructure-as-code for platform provisioning, and lead technical design while mentoring engineers.

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.

Lead design, build and modernize enterprise data platforms and pipelines, migrate on-prem processing to AWS, ensure secure, high-quality data delivery, collaborate with data scientists on ML deployment, and provide strategic leadership or subject-matter expertise.

Design, build, and maintain scalable data pipelines, warehouses, and lakes; ensure data quality, security, and operational stability; collaborate with data scientists to deploy ML models; lead strategy, governance, and delivery for data modernization and regulatory requirements.

Build and maintain secure, scalable data pipelines, warehouses, and lakes. Develop data processing and analysis algorithms, support machine learning model deployment, and manage cloud infrastructure and deployment pipelines. Collaborate with stakeholders, lead or advise technical teams, improve operational processes, and maintain risk, security, and regulatory controls across enterprise cloud and containerized environments.

Build and maintain secure, scalable data architectures, pipelines, warehouses, and lakes. Develop data processing and analysis algorithms, support machine learning model deployment, own platform defects, review code, and improve analytics platform design. Collaborate with quants, architects, technology teams, and stakeholders while ensuring compliance with security, privacy, accessibility, risk, and governance standards. The role also involves advising teams, coordinating resources, and driving continuous engineering and operational improvements.

Develop and maintain data extraction, transformation, automation, integration, and analytics solutions for Mastercard initiatives. Build complex SQL queries, Alteryx workflows, Tableau dashboards, and Power Platform applications. Gather requirements, deliver projects end to end, optimize reporting services, communicate quantitative insights, and collaborate with technical and business stakeholders to improve operational efficiency.

Designs, develops, tests, and supports Snowflake data pipelines and Python-based integrations across cloud and on-premises sources. Builds SQL transformations, data models, validation and reconciliation processes, monitoring, and automation. Troubleshoots data quality and production issues, supports deployments, documents technical processes, and collaborates with distributed Agile teams. The role also emphasizes secure data handling, governance, cost management, reliability, and continuous improvement.

Lead and execute acquisitions, disposals and strategic partnerships: identify targets, build pipelines, perform valuations and financial analysis, develop business cases, negotiate deal structures, manage post-merger integration, monitor performance and advise stakeholders. Supervise a team, embed risk controls, collaborate across functions, and maintain strategic alignment with group objectives.

Design, develop, and optimize secure, scalable cloud data platforms and ETL/ELT pipelines across AWS, Azure, and GCP. Write Python and SQL, implement data governance and quality controls, optimize performance and costs, and support analytics and BI layers. Contribute to architecture, pre-sales demos, proofs of concept, and client discussions. Mentor junior developers, conduct code reviews, and collaborate with architects, QA, DevOps, and business stakeholders.

Designs and develops scalable cloud data platforms and ETL/ELT pipelines using Python and SQL. Optimizes performance and costs, implements governance, security, quality, and compliance controls, and supports analytics consumption. Collaborates with architects and stakeholders, contributes to pre-sales prototypes and demos, mentors junior developers, conducts code reviews, and recommends improvements across AWS, Azure, and GCP environments.

Designs, builds, maintains, monitors, and optimizes cloud or on-premises ETL/ELT pipelines for large-scale batch and real-time data processing. Ensures data quality, security, compliance, reliability, scalability, and performance. Collaborates with business, technology, operations, governance, data quality, architecture, data science, DevOps, and BI teams to deliver production-grade data solutions.

Build and maintain scalable data architectures, ETL/ELT pipelines, data warehouses, and data lakes. Develop ingestion and transformation frameworks, data quality monitoring, CI/CD deployments, and performance optimization solutions. Ensure data security, compliance, documentation, and operational reliability while collaborating with data scientists and analysts to support machine learning and business needs. The role also involves advising stakeholders, managing risk and controls, influencing policy, and potentially leading and coaching a team.

Build and maintain scalable data architectures, ETL/ELT pipelines, warehouses, and lakes. Develop ingestion and transformation frameworks, data quality monitoring, and performance optimizations. Implement CI/CD, security, compliance, documentation, and operational troubleshooting. Collaborate with data scientists and analysts to support machine learning and business data needs. The role also involves advising stakeholders, managing risk and controls, influencing decisions, and potentially leading and coaching team members.

The Threat Intelligence Data Engineer designs and operates automated data collection systems for cybersecurity, focusing on discovering and indexing data from various web sources, including dark web and decentralized networks.

Lead design and implementation of AWS cloud data solutions using Python and PySpark. Build ETL pipelines, analytics (Glue, Athena, Redshift), and cloud architectures. Produce HLD/LLD, run cloud readiness assessments, cost estimates, DR planning, and provide technical leadership across onshore-offshore teams while liaising with customers and managed services.

Design, build, and maintain scalable AWS data platforms, implement data governance, and optimize architectures while collaborating with cross-functional teams.

Design, build, and maintain data pipelines, warehouses and lakes; implement ETL and processing algorithms; ensure security, monitoring and CI/CD; collaborate with data scientists and lead or coach team members to deliver data solutions.