Top Data Engineer Jobs in Pune

This role involves collaborating with users to build data solutions, managing AWS data technologies, and automating workflows for efficiency.

The Azure Data Engineer will implement big data pipelines using Azure technologies, collaborate with stakeholders, and utilize tools like Data Factory and Databricks.

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.

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.

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, 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.

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.

As a Data Engineer, you will develop and manage data pipelines, ensuring reliable data ingestion and delivery while optimizing for simplicity and performance.

The role involves designing scalable data pipelines and architectures using Microsoft Fabric, client engagement for requirements gathering, and optimizing data solutions for analytics and reporting.

As a Data Engineer, you will design, develop, and maintain data integration solutions, optimize ETL/ELT pipelines, and support data workflows for analytics and reporting.

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, build, and maintain scalable ETL/ELT and real-time data pipelines on AWS. Manage Airflow DAGs, Kafka ingestion, Redshift optimization, data quality checks, API integrations, and cost optimization while collaborating with BI for reporting needs.

The Senior Data Engineer will build and optimize ETL/ELT data pipelines using Azure Databricks and Apache Spark, ensure data governance, and collaborate with stakeholders to translate business needs into data engineering solutions.

Lead analytics initiatives by architecting enterprise Power BI dashboards, automating reporting with Python and Fabric, building and deploying ML/GenAI models, mentoring junior analysts, collaborating with finance for advanced analytics, and driving data governance and adoption across cross-functional teams.

Design, build, and maintain production data pipelines and integrations across applications, databases, and cloud services. Support data architecture, ingestion, embedding workflows for AI, data quality, monitoring, and platform reliability. Collaborate with Full Stack and AI teams to deploy scalable cloud-based data infrastructure and establish data engineering standards and best practices.

Design, develop, test, and maintain scalable Big Data systems and microservices. Own features, collaborate cross-functionally, leverage AI-assisted development tools, and improve engineering processes. Work with Spark, Hadoop, Kafka, containerization (Docker, Kubernetes, OpenShift) to solve complex technical challenges and deliver secure, high-quality solutions.

Design, build, and maintain data pipelines and models on GCP; integrate, cleanse, and prepare large datasets; implement data quality checks and security best practices; troubleshoot data issues; mentor junior data engineers and review their work.

Design, develop, test, and maintain high-quality Java-based Big Data systems and services. Own features, build scalable distributed systems and microservices using Spark, Hadoop, and Kafka. Use containerization and orchestration (Docker, Kubernetes, OpenShift), leverage AI-assisted development tools, collaborate cross-functionally, and improve engineering processes and code quality.

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.

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.