Top Data Engineer Jobs in Pune

Designs and builds secure, scalable data platforms and control-plane services across public cloud, on-premises, and sovereign environments. Architects lakehouse, distributed, batch, streaming, API, metadata, governance, and data-sharing solutions using modern cloud and open-source technologies. Establishes data-product, data-contract, security, reliability, and observability standards; develops production-quality software; optimizes infrastructure and costs; and mentors engineers while influencing technical direction across teams.

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.

Design, build, and maintain scalable data pipelines and cloud data platforms to support AI/ML workloads. Deploy, monitor, and operationalize machine learning models, implement CI/CD and MLOps, ensure data quality, scalability, and governance while collaborating with data scientists, engineers, and stakeholders.

Analyze business and user requirements across financial services value streams, define technical requirements, develop cloud solution options, and manage business and IT stakeholders. The role requires understanding existing framework capabilities, complex data models, cross-functional solution design, clear documentation, team coordination across regions, and familiarity with the SDLC from design through testing and implementation.

Design, develop, and maintain Snowflake-based data solutions and ETL pipelines. Ensure data quality, optimize pipeline performance, document workflows, and collaborate with teams while mentoring juniors.

Design, build and maintain data pipelines, data warehouses and data lakes on AWS. Develop processing and analysis algorithms, collaborate with data scientists to deploy ML models, lead or support a team, ensure data security, accuracy and compliance, and drive continuous improvement.

Designs and maintains scalable batch and real-time data pipelines on Azure. Builds integrations with Azure Data Factory, Storage Accounts, and Event Hub; optimizes SQL, stored procedures, and database workloads; develops data models for analytics and warehousing; implements data quality, monitoring, CI/CD, and deployment automation; collaborates with stakeholders and architects; and mentors junior engineers.

Design, develop, and maintain scalable data solutions, pipelines, and ETL processes using Databricks. Ensure data quality, reliability, governance, and efficient migration across systems. Collaborate with cross-functional teams to translate data requirements into technical solutions, troubleshoot pipeline issues, document workflows, and optimize performance. The role also involves contributing to team discussions, independently solving problems, and mentoring junior team members.

Design, develop, and maintain scalable data solutions, pipelines, and ETL processes using Databricks. Ensure data quality, governance, performance, and reliable data flow across systems. Collaborate with cross-functional teams to deliver data infrastructure, troubleshoot pipelines, document workflows, and support junior team members. The role requires independent execution, subject-matter expertise, and at least three years of Databricks experience.

Design, develop, and maintain scalable data solutions, pipelines, and ETL processes using Databricks. Ensure data quality, support data migration across systems, optimize pipeline performance, troubleshoot issues, and document workflows. Collaborate with cross-functional teams to translate requirements into technical solutions and provide guidance to junior team members.

Design, develop, and maintain scalable data solutions, pipelines, and ETL processes using Databricks. Ensure data quality, reliability, governance, and efficient migration across systems. Collaborate with cross-functional teams to translate data requirements into technical solutions, optimize pipeline performance, document workflows, troubleshoot issues, and mentor junior team members.

Design, develop, and maintain scalable data solutions, pipelines, and ETL processes. Ensure data quality, reliability, governance, and efficient migration across systems. Collaborate with cross-functional teams to translate data requirements into technical solutions, troubleshoot issues, document workflows, optimize pipeline performance, and support junior team members. The role requires expertise in Databricks and familiarity with cloud-based data storage and processing.

Design, develop, and maintain scalable data solutions, pipelines, and ETL processes using Databricks. Ensure data quality, reliability, governance, and efficient integration across systems. Collaborate with cross-functional teams to translate data requirements into technical solutions, troubleshoot pipeline issues, document workflows, optimize performance, and support junior team members while contributing as a subject matter expert.

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, develop, and maintain data solutions, pipelines, and ETL processes using Snowflake. Ensure data quality, support data integration and migration across systems, and apply data modeling, governance, and database design practices. Collaborate with multiple teams, make technical decisions, mentor junior engineers, and improve data processes. The role requires expertise in Snowflake and leadership as a data engineering subject matter expert.

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.

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 maintain scalable batch and real-time data pipelines, cloud-native data platforms, data lakes, warehouses, and marts. Develop Spark, Kafka, Maxwell, dbt, and SQL solutions; optimize distributed processing and storage; implement data quality, observability, governance, automation, and CI/CD. Partner with Product, Engineering, Analytics, and business teams to deliver trusted datasets, semantic models, and dashboards.

Build and maintain scalable data pipelines, warehouses, lakes, ingestion processes, and transformation solutions. Develop PySpark, AWS, Snowflake, dbt, SQL, and cloud-based data architectures; support data governance, orchestration, security, and lineage. Collaborate with data scientists, stakeholders, and infrastructure teams to translate requirements into reliable ETL and ELT components, deliver project implementations, manage controls, and improve data accessibility, quality, and performance.

Build and operate scalable financial market data infrastructure, including source evaluation, ingestion, cleansing, modeling, real-time and batch processing, quality governance, reconciliation, monitoring, and data services. Develop Flink-based pipelines supporting trading, research, and AI applications. Partner with product, trading, compliance, procurement, and engineering teams to define data contracts, source strategies, and consistent financial data semantics across markets.