Top Data Engineer Jobs in Pune

Leads enterprise data quality strategy, architecture, platforms, frameworks, observability, governance, and automation. Designs reusable quality capabilities for batch, streaming, cloud, on-premises, and SaaS environments; establishes rules, metrics, alerts, and remediation workflows; integrates quality with metadata, lineage, catalogs, MDM, and data contracts; mentors engineering teams; evaluates tools; and applies AI and machine learning to anomaly detection, root-cause analysis, and trusted AI-ready data platforms.

Lead the architecture and development of scalable batch, streaming, and event-driven data platforms supporting analytics, machine learning, generative AI, and agentic AI. Build governed lakehouse architectures, reusable data products, ingestion and orchestration pipelines, and AI lifecycle capabilities. Drive security, governance, observability, reliability, performance, and cost optimization while translating R&D concepts into production systems. Provide hands-on technical leadership, architecture reviews, mentorship, and cross-functional collaboration.

Lead the architecture and hands-on development of scalable batch, streaming, and event-driven data platforms supporting analytics, machine learning, generative AI, and agentic AI. Build governed lakehouse architectures, reusable data products, ingestion and orchestration pipelines, and ML lifecycle capabilities. Establish standards for security, governance, testing, CI/CD, observability, reliability, and cost optimization while providing technical leadership, mentoring, architecture reviews, and cross-functional collaboration.

Develop, test, and maintain Python- and SQL-based data pipelines and ETL/ELT processes on Google Cloud Platform. Build BigQuery transformations, support data warehouse modeling, troubleshoot data quality issues, document solutions, and collaborate with engineers and analysts. The role requires strong GCP data engineering experience, including BigQuery and Pub/Sub, with preferred experience in Cloud Composer, Dataflow, Cloud Functions, Git, and Google Cloud certification.

Leads the design, development, and maintenance of scalable batch and real-time data pipelines across Azure and on-premises environments. Builds production-grade ETL/ELT solutions, optimizes performance and reliability, ensures data quality and compliance, and solves complex business problems. Collaborates with business and technology stakeholders, leads solution design and estimation, and coaches data and analytics talent.

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.

Build and operate a self-service data commercialization platform spanning web experiences, APIs, cloud infrastructure, lakehouse systems, and automation. Develop scalable data platforms using Python, Spark, Databricks, orchestration, Java, React, and cloud services. Design secure, reliable workflows with observability, access controls, infrastructure as code, CI/CD, and multi-cloud architecture. Collaborate on technical trade-offs and integrate distributed systems, databases, and AI-powered capabilities.

Build and operate scalable cloud data and lakehouse platforms, including self-service interfaces, automation, APIs, infrastructure, governance, and security. Develop backend services with Java and Spring Boot, web experiences with React and TypeScript, and data solutions using Python, Spark, Databricks, and orchestration tools. Design reliable workflows, distributed systems, integrations, and cloud infrastructure across AWS and/or Azure. Contribute to architecture, observability, testing, cost optimization, and AI-powered platform capabilities.

Design and build data services and pipelines supporting machine learning products. Develop automation tools for model deployment, maintain production data systems, improve infrastructure, participate in code reviews, and collaborate across engineering, data science, and product teams. The role requires expertise in distributed systems, large-scale data processing, CI/CD, container orchestration, and AI-enabled workflow improvements.

Execute large-scale data platform migrations to Snowflake, adapting approximately 1,000 datasets and pipelines. Build, test, monitor, and troubleshoot Airflow workflows while supporting migrations from Airflow 2 to 3, Confluent region changes, and Iceberg adoption. Ensure data quality and reliability using Python, advanced SQL, Spark, PySpark, Kafka, Snowflake, and AWS S3. Collaborate with data platform teams on modernization initiatives and meet time-sensitive delivery targets.

Build and administer enterprise QlikView and Qlik Sense platforms, develop dashboards and data models, and integrate analytics with Snowflake, AWS S3, and multiple databases. Responsibilities include platform upgrades, security, monitoring, troubleshooting, incident management, data ingestion, ETL, service governance, and stakeholder coordination. The role supports global BI users, drives automation and continuous improvement, and requires strong infrastructure, SQL, DevOps, and ITIL knowledge.

Designs and maintains data foundations, pipelines, ETL/ELT processes, integrations, and data models across cloud platforms. Ensures data quality, supports data strategy implementation, coordinates change and incident management, presents findings to stakeholders, and mentors engineers. The role partners with technical and business teams to improve data accessibility, implementation efficiency, and business value.

Designs, codes, tests, and manages scalable data acquisition, integration, transformation, and management pipelines using Azure, Databricks, SAP Data Services, and DevOps practices. Builds data structures supporting Data as a Service, develops cloud and edge infrastructure, manages CI/CD pipelines and containerization, and collaborates with analysts, architects, product owners, and technical teams to deliver data-driven solutions.

Build and maintain scalable AWS data platforms, including batch and streaming ETL/ELT pipelines, data lakes, warehouses, and analytics-ready data models. Develop SQL transformations and Python-based pipelines using AWS services such as S3, Glue, EMR, Redshift, and Athena. Monitor performance, reliability, and data quality; troubleshoot issues; apply security and scalability practices; and collaborate with architects, DevOps, QA, product teams, and business stakeholders.

Build and maintain scalable AWS data platforms and batch or streaming pipelines. Develop SQL transformations and Python ETL workflows using services such as S3, Glue, EMR, Athena, and Redshift. Design data lakes, warehouses, and analytics-optimized models while monitoring performance, reliability, security, and data quality. Troubleshoot pipeline issues, support deployments, and collaborate with architects, DevOps, QA, product teams, and business stakeholders.

Support, maintain, troubleshoot, and optimize production Databricks applications, Spark jobs, SQL queries, Delta Lake pipelines, clusters, workflows, and workspace configurations. Implement data quality checks, logging, alerting, governance, security, and cost-management practices. Collaborate on enhancements and bug fixes, reduce technical debt, document operational processes, and participate in EST-aligned support rotations.

Design, build and maintain data pipelines, warehouses and lakes. Implement ETL processes, data models and processing algorithms. Ensure data accuracy, security and accessibility. Collaborate with data scientists, lead teams or projects, advise stakeholders, and drive risk controls and governance.

Designs, builds, maintains, and optimizes cloud or on-premises ETL/ELT pipelines for large-scale batch and real-time data processing. Ensures reliability, scalability, data quality, security, and compliance. Collaborates with business and technology partners across data governance, architecture, modeling, science, DevOps, and BI. Mentors data engineers and independently leads solution design, estimation, and delivery.

Designs, builds, maintains, monitors, and optimizes scalable ETL/ELT pipelines for structured and unstructured data across Azure and on-premises environments. The role supports batch and real-time processing, data quality, security, compliance, and performance tuning. It collaborates with business, technology, operations, governance, architecture, data science, DevOps, and BI teams to deliver production-grade data and analytics solutions.

Leads the design, development, deployment, and support of scalable data pipelines and platforms across Hadoop, on-premises, and AWS cloud environments. Builds batch and near-real-time processing frameworks, modernizes ETL and data warehouse workloads, implements governance and observability capabilities, and troubleshoots production issues. Mentors engineers, collaborates with cross-functional stakeholders, evaluates emerging technologies, and ensures secure, high-quality delivery for enterprise-scale data ecosystems.

Designs and develops scalable data management solutions, including data acquisition, integration, transformation pipelines, data structures, and cloud or edge infrastructure. Uses Azure Data Factory, ADLS, Databricks, SAP Data Services, DevOps pipelines, and containerization. Collaborates with analysts, architects, product owners, and technical leads to define requirements, deliver high-quality code, and support application enhancements.

Design, build and maintain secure, scalable AWS data pipelines, data lakes and Snowflake-based warehouses. Develop ETL/ELT (DBT, PySpark), orchestration (Airflow), advanced SQL/PLSQL, and collaborate with data science to deploy ML models while leading teams and ensuring governance, controls and stakeholder alignment.

Build and maintain petabyte-scale data pipelines, entity-centric data models, and distributed workflows using Databricks and Spark. Partner with data science teams to productionize ML features and artifacts, optimize ETL/ELT processes, and implement testing, monitoring, observability, and incident response. Collaborate with governance and operations teams to ensure secure, compliant data management while supporting scalable merchant identity and trust-profile systems.

Develop AI-powered product features for local discovery, including personalized recommendations, content curation, search, and ranking. Build data pipelines by scraping, cleaning, and structuring datasets for machine learning models. Collaborate with product, design, and engineering teams to integrate AI into the platform, while continuously improving machine learning capabilities and user experiences.

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.