Top Data Engineer Jobs in Pune

Design and build scalable data and AI platforms, including trusted data foundations, batch and streaming pipelines, ML systems, GenAI and agentic AI solutions. Develop RAG, vector search, tool-calling, APIs, workflows, governance, security controls, monitoring, and observability. Contribute to architecture, testing, CI/CD, technical design, code reviews, and engineering standards while mentoring engineers and enabling reusable supply-chain intelligence capabilities.

Design and deliver cloud-native data platforms, scalable big data pipelines, lakehouses, warehouses, transformation frameworks, BI solutions, and ML-enabled data workflows. Lead client-facing pre-sales engagements, technical workshops, proof-of-concepts, executive conversations, and enterprise data modernization initiatives. Build reusable architectural IP, influence senior stakeholders, and translate ambiguous business challenges into production-ready solutions using AWS and modern data technologies.

Design and maintain batch and real-time data pipelines, data lakes, warehouses, marts, and reusable dbt models using Spark, Kafka, Maxwell, S3, Trino, BigQuery, and related technologies. Optimize distributed processing, data models, queries, storage, reliability, and costs. Establish data quality, observability, governance, automation, and CI/CD practices while partnering with Product, Engineering, Analytics, Data Science, and business stakeholders.

Leads end-to-end data engineering initiatives spanning Fivetran ingestion, dbt transformations, Snowflake warehousing, and Power BI consumption. Designs scalable data models and pipelines, troubleshoots ELT and SQL issues, documents technical solutions, and partners with business stakeholders. Mentors engineers, establishes development standards, supports CI/CD, and pilots GenAI applications for documentation, testing, data quality, and natural-language querying.

Build secure, scalable AWS serverless applications focused on identity, data engineering, APIs, and customer engagement. Develop Python Lambda functions, React frontends, REST and GraphQL APIs, Cognito authentication workflows, and ingestion pipelines. Provision cloud infrastructure with Terraform or CloudFormation, support CI/CD, enforce security and compliance practices, integrate Amazon Connect, and troubleshoot application performance. Collaborate with architecture and DevOps teams in Agile development processes.

Lead the architecture and hands-on delivery of GCP data and generative AI solutions. Build scalable batch and streaming pipelines, REST APIs, RAG systems, agentic workflows, and cloud infrastructure. Own production deployment, CI/CD, security, governance, testing, resilience, monitoring, and L3 issue resolution while providing technical guidance on complex engineering decisions.

Leads the architecture and hands-on delivery of GCP-based data engineering and generative AI solutions. Builds batch and streaming pipelines, REST APIs, RAG systems, agentic workflows, conversational AI applications, and cloud infrastructure. Owns production deployment, CI/CD, security, governance, testing, resilience, monitoring, and L3 incident resolution. Provides technical guidance on system design and complex engineering decisions.

Lead the architecture and hands-on delivery of production-grade data and generative AI solutions on GCP. Build RAG systems, agentic workflows, REST APIs, batch and streaming pipelines, and automated data-quality processes. Manage infrastructure as code, CI/CD, security, governance, monitoring, and production support. Provide technical guidance, resolve complex L3 issues, and operationalize AI applications using Python, SQL, Terraform, containers, and core GCP services.

Build secure, scalable full-stack applications and data-intensive systems on Google Cloud. Develop frontend interfaces, Python services, REST APIs, batch and streaming pipelines, analytical models, and generative AI solutions including RAG and agentic workflows. Manage infrastructure, CI/CD, observability, security, testing, production readiness, incident resolution, governance, and cloud cost optimization. Contribute to architecture, technical standards, documentation, code reviews, and cross-functional engineering collaboration.

Build secure, scalable full-stack applications, APIs, cloud data pipelines, and AI capabilities on Google Cloud. Responsibilities include frontend and backend development, batch and streaming data engineering, RAG and agentic AI integration, infrastructure provisioning, CI/CD, observability, production support, security, testing, and architecture contributions.

Leads architecture and hands-on delivery of GCP data and generative AI solutions. Designs RAG systems, agentic workflows, conversational AI applications, REST APIs, and scalable batch and streaming pipelines. Uses Python, SQL, Vertex AI, BigQuery, Cloud Composer, Cloud Run, Terraform, and CI/CD tooling. Owns infrastructure, security, governance, testing, deployment, monitoring, and L3 production support while providing technical guidance on complex engineering decisions.

Designs, builds, deploys, and supports secure, scalable cloud data solutions and pipelines for enterprise clients. Translates business requirements into data architectures, integration patterns, and models; ensures performance, reliability, governance, and documentation. Provides technical leadership during reviews and workshops, collaborates with engineering and analytics teams, contributes reusable accelerators and best practices, mentors colleagues, and manages client engagements through delivery.

Build, monitor, optimize, and maintain cloud-based ETL/ELT pipelines supporting business intelligence and reporting. Develop high-performance SQL transformations, manage data warehouse schemas, tune query and pipeline performance, and optimize cloud costs. Collaborate with analysts, data scientists, actuaries, and architects to translate insurance and SaaS requirements into scalable technical solutions. Participate in code reviews, documentation, troubleshooting, and data integrity initiatives.

Design, build, and support production-grade AWS data pipelines that transform operational data into secure, high-quality, AI-ready datasets. Responsibilities include distributed data processing, Parquet curation, privacy-preserving transformations, orchestration, data quality monitoring, schema management, CI/CD, infrastructure as code, metadata and lineage management, troubleshooting, and reliable backfills.

Build production-grade agentic AI systems that reason, search, and execute workflows. Develop RAG pipelines, AI agents, semantic search layers, data processing solutions, dimensional data models, and scalable AI workloads. Work with structured and unstructured data, LLMs, vector databases, cloud infrastructure, CI/CD, and ML observability. Responsibilities include fine-tuning models, prompt engineering, optimizing context windows, and deploying reliable AI applications.

Designs and operates Databricks data platforms, pipelines, workflows, CI/CD processes, and cloud infrastructure on Azure. Responsibilities include cluster and workspace management, automated testing, Infrastructure as Code, access controls, FinOps, Airflow orchestration, monitoring, incident response, and reusable Terraform and Databricks templates. The role also improves platform reliability, security, developer productivity, and production code promotion processes while partnering with engineering, architecture, and security teams.

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.

Develops and supports big data applications and infrastructure using technologies such as Spark, Hadoop, cloud services, and workflow orchestration tools. Responsibilities include coding, code reviews, testing, production incident support, release deployments, root-cause analysis, integration, documentation, and continuous improvements to scalability, reliability, availability, and performance. The role also provides Level 3 technical support, assists junior team members, and collaborates across the software development lifecycle.

Designs and maintains scalable data pipelines using AWS, Spark, Kafka, Airflow, and related technologies. Collaborates with product, data science, and business teams; develops reporting dashboards; documents data flows and runbooks; optimizes workflows; troubleshoots data processing issues; and supports Agile delivery and CI/CD practices.

Develop, optimize, and maintain scalable ETL/ELT data pipelines for structured and unstructured data using Python and PySpark. Ensure data quality through validation, cleansing, transformation, and reconciliation. Collaborate with technical and business stakeholders, troubleshoot performance issues, conduct code reviews, and implement coding standards and CI/CD pipelines. Work with big data frameworks, cloud platforms, databases, data models, schemas, and warehousing solutions.

Design and operate secure, scalable Databricks-based data products and pipelines for member communications. Lead data architecture, integrations, governance, observability, quality, reconciliation, deployment, and production reliability initiatives. Partner with Product, Analytics, Privacy, Compliance, Risk, and Security teams while mentoring engineers, conducting reviews, resolving complex incidents, and improving performance, cost, and operational effectiveness.

Design, develop, and maintain scalable Microsoft Fabric and Azure data solutions. Build Lakehouse, Data Warehouse, OneLake, ETL/ELT pipelines, data transformations, and integrations using Python, PySpark, SQL, and Fabric tools. Implement medallion architecture, optimize data models and workloads, establish data quality monitoring, and support CI/CD deployments. Collaborate with analytics and business teams to deliver enterprise-scale data platforms.

Build and optimize scalable real-time and batch ETL/ELT pipelines for manufacturing analytics using Azure Databricks, PySpark, and distributed computing. Design cloud data lakes, lakehouses, and warehouses; process IoT, MES, SCADA, LIMS, and ERP data; implement CI/CD, DevOps, Docker, and Kubernetes practices; monitor pipeline reliability; and provide clean data for AI/ML models. The role also involves data preprocessing, cloud optimization, troubleshooting, and collaboration with automation, engineering, and data science teams.

Build and maintain batch and streaming data pipelines across AWS, Azure, and GCP using Databricks, Spark, PySpark, SQL, and Delta Lake. Implement medallion architecture, CDC, SCD, data quality, governance, security, metadata, and lineage controls. Configure storage, orchestration, CI/CD, monitoring, troubleshooting, and performance optimization. Collaborate with architects, data scientists, engineers, analysts, and product teams while documenting pipelines, transformations, testing, and operational procedures.