Top Data Engineer Jobs in Pune

Designs and deploys machine learning, analytics, NLP, and generative AI solutions for banking risk and compliance. Builds data pipelines, predictive models, dashboards, and production AI applications while supporting model governance, validation, explainability, and regulatory readiness. Partners with risk, compliance, data, and technology stakeholders; mentors junior team members; and contributes to AI strategy, model monitoring, and responsible AI practices across the organization.

As a Senior Data Engineer, you will develop and maintain data pipelines for healthcare data, ensuring data quality, reliability, and adherence to enterprise standards while collaborating with architects and stakeholders.

The Senior Data Engineer will develop data ingestion, transformation, and enrichment pipelines, ensuring data quality and reliability. Responsibilities include creating Bronze and Silver data layers, using SQL and dbt for implementation, and collaborating with architects and stakeholders to align data solutions with enterprise standards.

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.

Designs and leads enterprise business intelligence and analytics solutions using Power BI, Microsoft Fabric, Snowflake, Azure Data Factory, and modern data architectures. Responsibilities include developing semantic models, datasets, dashboards, data pipelines, data products, and scalable data platforms; implementing governance, quality, lineage, and metadata practices; enabling self-service analytics; optimizing performance; evaluating emerging tools such as Promethium; and advising stakeholders while mentoring data teams.

Designs and maintains enterprise data architecture, including conceptual, logical, and physical data models. Establishes data standards, metadata conventions, governance policies, security frameworks, and data dictionaries. Leads Master Data Management initiatives to improve consistency and integrity across platforms. Collaborates with business, analytics, application, and engineering teams, while supporting database, data warehouse, integration, and ETL pipeline efforts aligned with business, regulatory, security, and performance requirements.

Design, build, and maintain data pipelines, data warehouses and lakes; implement data processing and analysis algorithms; collaborate with data scientists to deploy ML models; lead performance engineering, system stability and scalability efforts; partner with cross-functional teams and ensure compliance with controls and service-level expectations.

Design, build and maintain data pipelines, warehouses and lakes; develop processing and analysis algorithms; collaborate with data scientists to deploy ML models; lead teams, drive performance engineering, and ensure security, reliability and compliance of data platforms.

Build and maintain data pipelines, warehouses and lakes; design processing and analysis algorithms; ensure data accuracy, security and performance; collaborate with data scientists to deploy ML models; lead or support team members and embed risk controls and performance engineering throughout the SDLC.

Build, optimize, and maintain scalable AWS-based data pipelines and data lakes (Snowflake/Databricks). Implement streaming ingestion (Kinesis/Kafka), CDC, automated data quality checks, and ML-ready feature stores. Collaborate with Product, ML, and Analytics teams, own pipeline QA, observability, and participate in on-call rotations to resolve production data issues.

Build, monitor, optimize, and maintain ETL/ELT pipelines using Google Cloud technologies. Develop complex SQL transformations, design dimensional data models, improve query performance and cloud cost efficiency, and ensure data integrity. Collaborate with analysts and stakeholders to translate SaaS and insurance metrics into technical solutions. Participate in code reviews, documentation, troubleshooting, and data warehouse lifecycle management while supporting US business hours.

Design, build, and optimize scalable GCP data pipelines and BigQuery data warehouses. Develop ETL/ELT workflows, implement data modeling, ensure data quality, optimize query performance and costs, maintain orchestration with Cloud Composer/Dataflow/Pub‑Sub, and automate deployments with CI/CD while enforcing security and governance.

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.

Develop and maintain reliable data pipelines, ELT processes, workflow orchestration, custom data connectors, and CI/CD pipelines using Airflow, Python, PySpark, Hive/Trino, SQL, and Snowflake. Collaborate with cross-functional teams, monitor and troubleshoot data workflows, validate and test data, document technical processes, and support data governance.

Own the end-to-end real-time voice and data platforms, including AI agents for live calls, telephony and streaming integrations, call-data pipelines, analytics, dashboards, monitoring, and cloud infrastructure. Build AI workflows, product interfaces, and business automations; optimize latency, cost, reliability, and performance; respond to production issues; and mentor teammates while collaborating with product and domain experts.

Leads end-to-end data products from ingestion and transformation through semantic modeling, Power BI reporting, documentation, governance, and user adoption. Builds Microsoft Fabric pipelines, Lakehouse or Warehouse architectures, dimensional models, and secure, optimized Power BI solutions. Analyzes data, supports advanced analytics and AI-assisted reporting, establishes quality and CI/CD practices, collaborates with cross-functional stakeholders, and manages data engineers through objectives, reviews, and development.

Design, build, and maintain data pipelines, warehouses and lakes; ensure data accuracy, security and scalability; develop processing and analysis algorithms; support ML model deployment; lead or coach team members and influence cross-functional decisions while managing risk and controls.

Designs, develops, and manages enterprise-scale data platforms and ETL/ELT pipelines using dbt, Snowflake, Airflow, SQL, Python, and GitLab CI/CD. Responsibilities include data modeling, warehouse optimization, workflow monitoring, data governance, quality, observability, and performance tuning. The role supports AI/ML initiatives through feature-ready datasets and pipeline integration, collaborates with technical and business stakeholders, mentors team members, and contributes to architecture and engineering best practices.

Lead design and implementation of an Analytic Foundation and scalable big-data services delivered via APIs. Build data pipelines, analytics and data models, responsive UIs, and production ML/data-driven applications. Ensure high-quality, well-tested code, mentor junior engineers, drive process improvements, and collaborate with product and design teams to deliver customer-focused data solutions.

Design, build, and maintain scalable ETL/data pipelines and data management blueprints. Ingest, transform, and curate large complex datasets for analytics, support cloud (AWS) implementations, enable downstream consumers, and provide technical leadership and operational support to ensure data governance, quality, and automation.

Design and develop scalable data-driven solutions and pipelines using Python and SQL. Leverage Azure data services to build secure, high-performance architectures, ensuring data integrity, governance, and overall success of data projects.

Design and build scalable data pipelines and architectures on Azure using ADF, Databricks, and Synapse. Implement Kimball/Data Vault modeling, write ETL in Python and T-SQL, ensure data governance (Azure Purview), automate tests, troubleshoot issues, document processes, and collaborate across teams.

Design and develop data-driven solutions and scalable data pipelines using Python and SQL. Leverage Azure data products to build robust, secure architectures, ensuring data integrity, performance, and governance while supporting data-centric client projects.

As a hands-on data engineer, you'll build and enhance scalable data platforms, develop data pipelines, and collaborate with various teams to improve data solutions and system performance.

Build governed Databricks Lakehouse foundations and productionize machine learning and GenAI solutions, including forecasting, anomaly detection, NLP, RAG, and predictive analytics. Develop reliable batch and streaming workflows, CI/CD, monitoring, governance, security, lineage, and semantic models. Collaborate with product, data, platform, and domain teams to deliver measurable customer value while meeting reliability, compliance, cost, and operational objectives.