Top Data Engineer Jobs in Pune

Design, develop, and coordinate cloud data solutions using Snowflake, Python, and AWS. Support product releases, deployments, testing, and post-install support. Collaborate with architects and tech leads, troubleshoot issues, and maintain documentation. Contribute to agile squads, adopt new technologies, and enable Fidelity Wealth business data needs.

Design, build, and maintain cloud data pipelines and architectures using SQL, Python, Snowflake, and AWS. Implement CI/CD for data workflows, use developer assistance tools, and collaborate across teams to deliver reliable data solutions.

Design, build and maintain ETL mappings and data models; write SQL and Snowflake queries; manage ETL/infrastructure on AWS (EC2); use Informatica, Control-M and ServiceNow; script in Python/Shell/PowerShell; collaborate with onshore/offshore teams and define BI delivery strategy.

Develop and maintain data pipelines and applications using Python and Pandas, apply OOP and data structures, write and optimize SQL, and communicate with stakeholders. Familiarity with Agile, DevOps/CI-CD tools (Jenkins, Git, Jira), AWS, web frameworks (Django/Flask), and Snowflake is a plus.

Lead database design, data modeling, migrations, and schema evolution across MySQL/Postgres and GCP databases. Design and operate config-driven ETL/ELT pipelines and streaming ingestion, enable CI/CD, troubleshoot data flows, and mentor offshore DB/ETL engineers.

Perform data migration/refresh from production, support Python-based data refresh framework, run post-migration fixes, validate and reconcile data, monitor and resolve batch jobs and pipeline issues, perform performance checks, and coordinate with dependent teams.

Design, build, and maintain ELT/data pipelines and workflow orchestration using Airflow, Python, and PySpark. Develop custom connectors, implement DataOps and CI/CD for data, monitor and validate data flows, collaborate with cross-functional teams, and document data workflows and governance.

Join the Event Streaming and Data Replication platform as a Data Engineer to design, build, and operate cloud-native data platforms. Work with Airflow, Spark, Kubernetes, and Python; deploy containerized apps on AWS/Azure; use IaC (Terraform/CFT/ARM); implement CI/CD (GitHub, Artifactory, Jenkins); configure monitoring and alerts; and collaborate across teams to deliver highly available, scalable solutions.

Design, build, and maintain scalable ELT/data pipelines and connectors using Airflow, Python, and PySpark. Implement DataOps and CI/CD for data workflows, monitor and troubleshoot pipelines, validate data quality, collaborate with cross-functional teams, and document data processes and governance.

Design, build, and maintain ELT/data pipelines and workflow orchestration using Airflow, Python, and PySpark. Develop custom connectors to ingest diverse data, implement DataOps and data CI/CD pipelines, monitor and troubleshoot pipelines, validate data quality, collaborate with cross-functional teams, document workflows, and work with large-scale distributed datasets and visualization tools.

Builds scalable ETL pipelines, cloud and data-lake integrations, and analytical tools supporting global supply chain planning and inventory optimization. Applies statistical methods, machine learning, AI-assisted development, and data visualization to portfolio segmentation, product lifecycle management, replenishment strategies, and decision support. Collaborates with stakeholders, leads complex data engineering projects, improves database performance, and promotes secure, governed, enterprise-wide data accessibility.

Design, build, and optimize ETL pipelines and data warehouses using SQL, AWS Glue, and S3. Investigate and resolve data issues, develop Python-based solutions and small web apps, and own requirements, design, and end-to-end delivery for change requests.

Designs and executes enterprise Salesforce and Data Cloud migrations, including ETL, schema and relationship mapping, unstructured data ingestion, high-volume API automation, validation, deduplication, and governance. Prepares clean, standardized, vectorized data for Agentforce and RAG workflows. Works with legacy databases, Salesforce integration tools, SQL, SOQL, and scripting languages to support complex consolidations and AI-ready data architectures.

Provide onsite acquisition and analysis of drilling data during rig operations, install and maintain GEOLOG systems, perform real-time monitoring, generate technical logs and reports, act as first-line client technical support, and manage/train mud logging team while ensuring rig safety compliance.

Leads data management and quantitative analysis strategy, ensuring data integrity, accuracy, and governance across platforms. Translates complex data insights into business strategies, drives process automation and continuous improvement, and collaborates cross-functionally to support data-driven decisions. The role also mentors team members, promotes innovation, and advances data management practices through emerging technologies and industry trends.

Owner of company-wide reporting and analytics for Growth, Marketing, Product, Finance and Operations. Build and maintain dashboards, automated reports and data pipelines (dbt), ensure data quality and governance, run growth and product analyses (CAC, ROAS, LTV, retention), support A/B testing and ad-hoc decision support, leverage AI for automation, and coordinate VAs on data tasks.

The Data Engineer will build and manage scalable data pipelines and databases, collaborate on architecture, and drive data systems towards real-time capabilities in a fast-paced, innovative environment.

Design, implement, and maintain data pipelines using Elasticsearch and data warehousing solutions, integrate data sources, and ensure data accuracy while collaborating with cross-functional teams.

Develops production-grade data science solutions using statistical modeling, machine learning, causal inference, and experimental design. Responsibilities include data exploration, feature engineering, model validation, deployment packaging, explainability, model documentation, experiment tracking, and production pipeline collaboration. The role focuses on fraud and risk, predictive maintenance, and patient outcome modeling, while mentoring junior analysts and translating research into platform capabilities.

Validates data movement, mappings, transformations, APIs, migrations, AI-agent workflows, business rules, guardrails, and user-interface outputs. Develops integration, regression, edge-case, and AI-output tests; verifies data integrity and schema conformance; evaluates agent decisions, tool usage, fallbacks, hallucinations, and human-review handoffs. Investigates defect sources across systems and communicates quality risks and findings to product, engineering, data, and business stakeholders.

As a Staff Data Platform Engineer, you will lead the design and development of data infrastructure, manage CI/CD processes, and implement observability for data pipelines.

Design, build, and maintain dbt models and Snowflake data warehouses, own CI/CD and orchestration for pipelines, implement data quality tests, partner with stakeholders to translate requirements into production-ready data models, and optimize performance and observability.

Build and optimize Starburst-based data pipelines and federated queries, integrate databases, data lakes, and warehouses, and ensure performance, reliability, and governance. Collaborate with analytics, AI/ML, and business teams to deliver scalable data platforms and solutions.

Lead the implementation, configuration, administration, and integration of Alation Data Catalog. Manage metadata ingestion, data lineage, business glossaries, data quality, classification, stewardship, governance workflows, and platform integrations. Develop automation using Alation APIs, REST APIs, SQL, and Python; troubleshoot catalog operations; collaborate with technical and business stakeholders; and mentor Alation developers.

Design, develop, deploy, and support Salesforce Data Cloud solutions, including CDP implementations, custom applications, integrations, data pipelines, and optimized data models. Gather requirements, implement governance and security practices, troubleshoot performance issues, document solutions, and provide technical guidance to stakeholders and team members.