Top Data Engineer Jobs in Delhi

Design, build and maintain cloud-oriented big data solutions for ingestion, processing, cleaning and exposition. Improve data quality, implement ETL and data-warehouse architectures, and collaborate with DDS/Data Foundation teams to ensure platform reliability.

Design, implement, and optimize Apache DolphinScheduler workflows and ETL pipelines. Integrate with Hadoop, Spark, Flink, Hive, and Kafka. Develop custom plugins, troubleshoot performance and scalability issues, monitor job execution and resource allocation, and maintain CI/CD, version control, and infrastructure automation while collaborating with data engineers and DevOps teams.

Design, build, and optimize PySpark applications and ETL pipelines to process large-scale datasets from relational, NoSQL, file, and streaming sources. Ensure data quality, implement error handling, and collaborate with analysts, scientists, and architects to deliver performant data solutions.

Senior Java engineer responsible for building data lake and big data solutions (preferably on Spark) with cloud experience (Azure/AWS/GCP), REST services, OOA/OOD and JEE design patterns, PostgreSQL expertise, TDD practices, and working in Agile teams. Optional: front-end (Angular/React), Docker/Kubernetes, investment banking domain knowledge.

Design, model, and optimize relational and analytical data structures for OLTP and data warehouse systems. Build ER diagrams, define modeling standards, manage metadata with tools like Erwin/ER Studio, and drive data cataloging/lineage using Alation/Collibra. Perform performance tuning, sandbox POCs, handle time-series/history, and collaborate with engineers, architects, and product teams while documenting and enforcing governance.

Build and maintain large-scale PySpark/Spark data reconciliation pipelines on Hadoop/Big Data platforms; integrate with Kafka, APIs and microservices; support capital markets regulatory reporting and reconciliations; lead globally distributed teams, manage stakeholders, and deliver solutions with strong application design and optional UI integration.

Design, build, optimize, and deploy large-scale Spark/Scala data processing and ETL pipelines on Cloudera (CDH). Collaborate with data teams, ensure data integrity and security, troubleshoot performance, and implement version control and CI/CD for Spark applications.

Design, configure, and support backend software and APIs; manage configuration files and environments; test and document API endpoints; collaborate on system data flow; participate in large-scale live data migrations (billions of records) into FinCloud; produce documentation and troubleshooting guides; work in agile teams and support integration and observability efforts.

Design, build, and optimize end-to-end data pipelines for large structured and unstructured data. Implement near-real-time ETL, data validation, monitoring, and performance optimization. Collaborate with stakeholders, document designs and workflows, and provide technical guidance to the team.

Design, develop, test, and deploy high-performance Spark/Scala data processing applications and ETL pipelines on Cloudera (CDH). Optimize Spark and Cloudera platform performance, integrate HDFS/Hive/Impala/HBase/Kafka, ensure data integrity/security, and implement version control and CI/CD for production pipelines.

Develop, deploy, and maintain LLM pipelines and RAG QA/search systems; design and optimize prompts and multi-agent LLM architectures; operate multi‑GPU/cluster inference; build evaluation pipelines for model quality, bias, and hallucination; collaborate with product and CS teams to integrate conversational AI.

Build and scale an AI-powered stock research platform: design data pipelines ingesting SEC and market data, develop backend APIs, implement semantic search with embeddings and vector DBs, improve production reliability and performance, and own features end-to-end from design through deployment and monitoring.

Provide Level 3 escalation and expert technical support for enterprise IT infrastructure across multi-client data centre environments. Design, implement, troubleshoot, and optimize Windows Server, virtualization (VMware/Hyper-V/NSX), storage, backups, Active Directory, and hybrid Azure/Microsoft 365 systems. Standardize configurations, document SOPs, automate provisioning and patching, collaborate with network/security teams, mentor junior engineers, and lead Tiger Team projects.

The Senior Data Engineer will build and optimize data pipelines in Azure Databricks, migrate existing pipelines from Azure Synapse, and design scalable data models to support a financial services client's data platform modernization.

As a Senior Engineer for Strategic Data Solutions, you will manage data warehousing, reporting, and analytics for internal operations and external partners. Responsibilities include collaborating with business units, providing insights, and ensuring high-quality data reporting.

Responsible for guiding teams in utilizing technology effectively, mentoring colleagues and devising advanced solutions to meet client challenges.

As a Senior Engineer in Data Analytics, you'll work on challenging problems using cutting-edge technology alongside talented colleagues to drive innovative solutions.

The Senior Staff Engineer, Data Science role involves guiding team direction, solving complex challenges for clients, and leveraging technical expertise to innovate solutions.

The Principal Engineer, Data Science role involves leading data science projects and providing strategic insights using extensive IT experience.

The Big Data Engineer will design and develop scalable data solutions, build data pipelines, ensure data quality, and collaborate across teams.

Design and implement test plans, write test code, validate software/SDKs and AI models for geospatial AI products. Troubleshoot issues, collaborate with data scientists, create documentation, samples, tutorials, and develop pretrained geospatial AI models for imagery, 3D, and text.

Design and own end-to-end data platform architecture, build ingestion and transformation pipelines, model multi-tenant datasets, optimize Postgres and ClickHouse performance/cost, implement orchestration and observability, define data quality and reconciliation standards, and mentor data/product engineers.

Design, build, and scale cloud-native Python backend services and data-backed applications. Develop RESTful APIs, microservices, and asynchronous event-driven systems. Deploy and operate production workloads on Kubernetes and at least one public cloud (AWS or Azure). Work with cloud data warehouses (Snowflake or Databricks) and relational databases, ensuring scalable, reliable distributed systems.

Design, operate, and secure scalable batch and streaming data pipelines and platforms. Own ingestion, transformation, serving, data quality, and access controls. Partner with product, risk, compliance, and InfoSec to ensure production reliability, correctness, and audit readiness for financial data.

Design, build, and optimize scalable data pipelines and models using Spark, Kafka, Airflow, dbt, and SQL across GCP and AWS. Ingest and normalize marketing and analytics data, ensure data quality and governance, support analytics and reporting, and collaborate with cross-functional global teams.