Top Data Engineer Jobs in Pune

Build and optimize ETL/ELT pipelines feeding BigQuery, audit and troubleshoot data quality, and create reliable Looker Studio dashboards. The role supports sales leaders by turning raw data into accurate, actionable insights, improving reporting performance, eliminating manual processes, and ensuring data integrity across Curvion’s revenue operations ecosystem.

Design, build, and maintain scalable data pipelines and ETL/ELT solutions using Microsoft Azure Fabric, Databricks, and Azure data services. Translate business requirements into technical solutions, optimize data workflows for performance and cost, troubleshoot production issues, and collaborate with architects, analysts, developers, and global stakeholders. Use AI-assisted development and automation to improve engineering productivity while owning delivery quality, timelines, and outcomes.

Design, build, and deploy secure, scalable cloud data solutions and pipelines for enterprise clients. Translate business requirements into architectures, integrations, and data models; ensure production performance and reliability; lead technical reviews and workshops; create reusable documentation and accelerators; and collaborate with distributed engineering, analytics, platform, and client teams. The role also includes mentoring, delivery ownership, stakeholder communication, and contributing to consulting practice excellence.

Provides L1 production support for financial applications and enterprise data platforms. Responsibilities include monitoring ETL jobs, data pipelines, batch schedules, alerts, and incidents; troubleshooting data quality, reconciliation, ingestion, reporting, and application issues; performing SQL-based data updates, smoke testing, root-cause analysis, and impact assessment; coordinating with technical and business teams; escalating complex issues; supporting disaster recovery, audits, automation, and continuous service improvement.

Build full-stack tools, backend services, APIs, dashboards, and data workflows for assessing and improving AI training data and evaluations. Partner with quality and research teams to inspect agent trajectories, validate graders, measure quality, investigate workflow issues, and turn internal prototypes into reliable production platform capabilities.

Design and maintain analytical pipelines and reporting solutions using BigQuery and MariaDB. Develop Python and FastAPI tooling, APIs, and automation; write advanced SQL; apply AI/ML capabilities; document data architecture and lineage; and translate complex analysis into actionable business insights. Collaborate across departments on process improvements and independently own analytics projects end to end.

Design, build, and maintain scalable data pipelines and ETL/ELT workflows using Azure Data Factory, Databricks, PySpark, Python, and SQL. Transform large datasets, optimize data ingestion and queries, monitor pipeline performance, resolve data quality issues, support data modeling, document engineering standards, and contribute to cloud data infrastructure improvements. The role also involves collaboration with architects and senior engineers and requires familiarity with Azure Synapse, Delta Lake, governance, and security.

Lead backend and data-platform architecture while remaining hands-on with Java production code. Design and operate distributed systems for event ingestion, analytical querying, data processing, and low-latency serving. Work with ClickHouse, BigQuery, Bigtable, Kafka, Redis, GCP, and Dataflow to solve scalability, reliability, performance, and data-correctness challenges. Set technical direction, review designs, mentor engineers, and guide platform convergence across Wingify and AB Tasty.

Develop robotics datasets and evaluations for embodied AI systems. Responsibilities include defining data schemas, annotations, quality standards, collection protocols, validation tools, and review workflows. The role also involves running experiments to assess how data quality and structure affect model performance, collaborating with research, engineering, vendors, and customers, and taking research tools from conception through deployment.

Develop a Java-based desktop IDE, coordination server, and relational data backend for a semiconductor test engineering DSL. Responsibilities include architecture, data modeling, versioning, synchronization, language editing and validation, debugging, automated testing, and platform quality. Collaborate with international engineering teams and domain experts to build a reliable, extensible development environment.

Develop a Java-based desktop IDE, coordination server, and relational data backend for a semiconductor test engineering DSL. Responsibilities include desktop UI development, data modeling, client-server synchronization, language editing and validation, architecture, debugging, automated testing, and platform quality. The role involves shaping a modular development environment and collaborating with international engineering teams and domain experts.

Principal Data Engineer responsible for developing and supporting Databricks pipelines, integrating heterogeneous healthcare data sources, modeling unified data products, and ensuring data quality and availability. The role partners with business stakeholders, product teams, data engineers, and clinical leaders to gather requirements, create datasets, support ad hoc analytics, enable machine learning and AI use cases, and assist with acquisition integrations. Extensive healthcare data, cloud, SQL, PySpark, and Python experience is required.

Develop and maintain Python-based API data ingestion services for GCP. Build secure, scalable, fault-tolerant pipelines with authentication, incremental loading, validation, reconciliation, orchestration, monitoring, and alerting. Manage data quality, PII masking, CI/CD, production troubleshooting, performance optimization, SLAs, data contracts, technical designs, mappings, and operational documentation. Collaborate with stakeholders to define support models and ensure reliable ingestion from internal and external APIs.

The Threat Intelligence Data Engineer designs and operates automated data collection systems for cybersecurity, focusing on discovering and indexing data from various web sources, including dark web and decentralized networks.

Design, build, and maintain scalable AWS data platforms, implement data governance, and optimize architectures while collaborating with cross-functional teams.

Build and support fund accounting data solutions for asset management, including integrations, transformations, reconciliations, controls, and finance reporting products. Develop large-scale data pipelines using Databricks, Python, PySpark, and SQL; work with investment data, NAV calculations, accruals, corporate actions, fees, and general ledger information. Support cloud-based delivery, data quality, orchestration, CI/CD, and direct collaboration with US stakeholders.

Operate and improve production data infrastructure across large-scale distributed cloud systems. Monitor performance, manage alerts, respond to incidents, perform root cause analysis, automate repetitive tasks, support CI/CD pipelines, document runbooks, and assist with capacity planning. The role requires participation in an on-call rotation and collaboration with Engineering to maintain reliability, efficiency, and uptime.

Provides first-line production support for enterprise applications by monitoring Splunk and New Relic dashboards, investigating alerts, triaging incidents, managing tickets, documenting evidence, escalating issues, and supporting maintenance and deployment windows. The role requires monitoring knowledge, incident management skills, clear communication, and the ability to handle multiple production incidents. Familiarity with AWS data platforms and Python, SQL, Scala, or Shell is preferred.

Leads data engineering initiatives involving ETL/ELT pipelines, database optimization, orchestration, big data technologies, data modeling, BI performance, and data quality monitoring. Requires strong Scala, Python, SQL, and shell scripting skills, plus payments-domain expertise covering transaction processing, settlements, disputes, reconciliation, gateways, acquiring, issuing, and PCI-DSS compliance. The role also involves cross-functional communication, planning, facilitation, negotiation, and collaboration with senior stakeholders and clients.

Design, implement and support analytical data infrastructure and real-time pipelines on AWS. Extract, transform and load data from varied sources using SQL and big-data tools; enable analytics, monitoring, and machine-learning workflows; improve reporting and automate self-service data processes.

The Senior Azure Data Engineer will design, develop, and maintain data infrastructure and pipelines, ensuring data quality, integrity, and alignment with business objectives. Responsibilities include collaborating with teams and establishing best practices for data engineering.

Design, build, and maintain Azure-based data infrastructure and pipelines. Implement data models and schemas, ensure data quality, optimize distributed storage and processing (Databricks/PySpark), and establish engineering best practices while partnering with cross-functional teams.

Design, build, and maintain enterprise ETL/ELT pipelines in Microsoft Fabric; ingest and transform data from diverse sources into dimensional star schemas; optimize performance and cost; implement data quality checks and monitoring; document architecture and pipelines; collaborate with analysts, stakeholders, and global engineering teams to deliver Power BI-ready analytics datasets.

Lead design, develop, test, and maintain cloud-native risk platform components on AWS. Build containerized systems (EKS/Docker/Helm), backend services in Python using Boto3, implement IaC and CI/CD, collaborate with architects and stakeholders, and support risk analytics, data modeling, reporting, and production troubleshooting.

Design and build scalable data and AI infrastructure for production LLM applications. Responsibilities include developing RAG systems, embedding and vector retrieval pipelines, AI agents, LLM evaluation and observability frameworks, and batch or streaming data platforms using Databricks, Spark, Snowflake, Delta Lake, and Airflow. The role also involves optimizing distributed workloads, establishing data governance and quality practices, and partnering with engineering and product teams to productionize AI capabilities.