Top Data Engineer Jobs in Pune

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.

Administer and optimize Cloudera CDP/CDH environments and enterprise data-processing workloads. Develop PySpark and Python solutions, build CI/CD pipelines with Jenkins and Git repositories, integrate security and code-quality tools, optimize Hive and Impala SQL, monitor platform health, perform upgrades, troubleshoot issues, and implement security, access management, governance, and high-availability practices.

Develop, test, validate, and support large-scale batch and real-time data platforms using Spark, Kafka, and NiFi. Build resilient data pipelines, validation frameworks, reusable platform components, and deployment automation. Optimize distributed systems for performance, scalability, and reliability; support production operations, monitoring, troubleshooting, and incident resolution. Collaborate on architecture, CI/CD, documentation, and engineering standards while mentoring junior engineers.

Designs, develops, and maintains enterprise data solutions, pipelines, data foundations, and ETL/ELT processes. Ensures data quality, integrates and migrates data across cloud systems, and supports data modeling and processing. The role partners with technical and business stakeholders, leads data strategy implementation, coordinates change and incident management, presents findings, mentors junior engineers, and drives scalable, cost-effective delivery.

Designs, develops, and maintains data foundations, pipelines, ETL/ELT processes, and data integration solutions. Ensures data quality, supports migration across systems, and translates data strategy into scalable technology solutions. Collaborates with technical and business stakeholders, presents findings, leads implementation initiatives, manages change and incidents, and mentors junior engineers. Required expertise includes Azure, Databricks, data modeling, cloud data platforms, orchestration, streaming, and SQL technologies.

Design, develop, and maintain data solutions, pipelines, data models, and ETL processes. Ensure data quality, reliability, and efficient data migration across systems. Collaborate with multiple teams, provide technical solutions, make team decisions, and serve as a subject matter expert. Monitor and troubleshoot pipeline performance while supporting analytical and operational data needs.

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.

Design, build and maintain data pipelines, data warehouses and lakes using PySpark, Snowflake and AWS analytics services. Implement scalable ETL/ELT, collaborate with data scientists for ML deployment, ensure data governance, security and high-quality deliverables while leading or advising team members.

Build scalable data pipelines and ETL/ELT processes using Python and modern data platforms. Perform exploratory analysis, validate data, develop data models, and support analytics and real-time insights. Collaborate with data scientists, analysts, engineers, and product teams while delivering tested, reliable features. The role also contributes to data quality policies, processes, scorecards, and product operations reporting.

Build scalable data pipelines and ETL/ELT processes using modern data platforms. Perform exploratory analysis, validate data, develop models supporting analytics and real-time insights, and collaborate with data science, product, engineering, and customer success teams. Write tested Python code, contribute to data quality initiatives, and deliver end-to-end features in an agile environment.

Build and support scalable healthcare data platforms, unified data models, datasets, and ad hoc data access solutions. Extract and integrate heterogeneous healthcare data, improve data quality, support acquisitions, and model data for BI, software applications, machine learning, and AI. Collaborate with product, engineering, business, and clinical stakeholders while following software development lifecycle best practices.

Maintains, cleans, manipulates, and secures operational and analytics databases. Designs scalable data pipeline architecture, builds complex data sets and analytics tools, automates manual processes, improves infrastructure, troubleshoots database issues, and supports software engineering, data science, analytics, product, design, and executive stakeholders.

Maintains, cleans, manipulates, and secures operational and analytics data. Designs scalable data pipeline architectures, builds complex data sets and analytics tools, automates manual processes, improves infrastructure, troubleshoots database issues, and supports data, product, design, analytics, and data science teams with technical data needs.

Design, build, and maintain scalable Azure-based data pipelines and systems (Databricks, Data Lake, Synapse, ADF). Collaborate with cross-functional teams, ensure data quality and integrity, optimize performance, monitor and troubleshoot systems, and stay current with industry trends to improve solutions.

Designs and optimizes scalable data pipelines and architectures supporting analytics and operational needs. The role develops ETL, streaming, data quality, governance, and cloud-based workflows using modern big data technologies. Responsibilities include translating business requirements into technical solutions, monitoring data systems, improving reliability and performance, and providing technical guidance on data modeling, storage, and metadata management.

Build and support data integration and real-time streaming platforms. Responsibilities include data modeling, transformation, cloud-based data engineering, CI/CD and DevOps practices, Kafka and CDC solutions, topic and schema management, connector and stream-processing pipelines, legacy-to-modern platform integration, data mapping, quality management, and stakeholder collaboration.

Leads development of secure, scalable big data pipelines and data engineering solutions using Spark, Scala, Python, Java, Hadoop, object storage, and NiFi. Drives data ingestion, movement, access automation, engineering standards, and process improvements. Collaborates with stakeholders and distributed teams, resolves production incidents, performs root-cause analysis, evaluates new technologies, and provides technical leadership to junior engineers.

Build and maintain large-scale backend data systems, batch and streaming pipelines, datamarts, and cloud data workloads. Develop AI-powered agents for anomaly detection and workflow automation, using Scala, Python, Java, Spark, Cloudera, Databricks, and AWS. Support platform reliability, data integrity, downstream analytics, documentation, Agile collaboration, and team mentoring.

Build data engineering solutions that ingest, aggregate, unify, and analyze high-volume data from diverse sources. Develop data pipelines and models using Cloudera, Databricks, AWS, Hadoop, Python, and SQL. Partner with product, data science, platform, technology, client, and sales teams to deliver actionable insights, prototypes, business intelligence, and innovative cyber, fraud, security, and digital identity products.

Develops, tests, validates, and supports large-scale batch and real-time data platforms using Spark, Kafka, and NiFi. Builds resilient data pipelines, validation frameworks, distributed storage solutions, deployment automation, and operational tooling. Optimizes performance, scalability, and reliability; supports production troubleshooting and incident resolution; contributes to architecture, CI/CD, documentation, code reviews, and engineering standards; and mentors junior engineers.

Build and maintain scalable ETL/ELT pipelines, cloud data infrastructure, automated workflows, and cross-platform integrations. Manage relational databases and cloud warehouses, optimize data access, and route clean data to AI systems, dashboards, and reporting tools. Implement data quality checks, deduplication, monitoring, governance, and security controls. Collaborate with AI, backend, and marketing teams while managing multiple client integration projects remotely.

Designs and develops scalable Microsoft Fabric and Azure data platforms, including ETL/ELT pipelines, APIs, integrations, data warehouses, lakehouses, and analytical data models. The role supports Power BI reporting, Salesforce integration, data quality, monitoring, CI/CD, performance optimization, production troubleshooting, and collaboration with business, CRM, reporting, and engineering teams.

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.

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.

Build and operate scalable financial market data pipelines for Binance stock products and AI applications. Responsibilities include sourcing, ingesting, modeling, processing, storing, and serving real-time and historical data; designing multi-market data frameworks; optimizing Flink pipelines; and establishing quality, reconciliation, monitoring, lineage, and recovery systems. The role also evaluates vendors and data sources, defines data contracts, and collaborates with trading, product, AI, legal, and compliance teams.