Top Data Engineer Jobs in Delhi

Design and implement AI-powered data pipelines and workflows combining LLMs, embeddings, vector search, and RAG to transform structured and unstructured data into business-ready outputs. Build APIs and full-stack integrations, produce Excel/PDF/Word deliverables, optimize relational and vector data stores, and prototype scalable AI solutions while contributing to engineering best practices.

Build data infrastructure and reliable pipelines, analyze product usage, train traditional and LLM-based models, define product metrics, run experiments, and translate findings into product and go-to-market decisions. The role combines data science, data engineering, product analytics, machine learning, and stakeholder communication, with ownership across data modeling, evaluation, experimentation, and product insight generation.

As a Python and Kubernetes Software Engineer, you will develop data analytics solutions using open source tools, collaborating with a distributed team.

Build, operate, and scale ingestion pipelines for marketing data (ad platforms, ecommerce, analytics). Normalize and transform data into shared schemas, ensure multi-tenant reliability, observability, and security, and partner with AI/data-science teams to expose clean data for agents.

The Senior Data Engineer will design and build data infrastructure and pipelines, enhance AI capabilities, and collaborate with teams on data solutions while mentoring junior engineers.

The Data Architect will design and implement data infrastructure, collaborating with teams to enhance data usage for analytics, automation, and AI, while ensuring compliance and governance.

Designs and operates scalable data and machine learning platforms using Databricks, Python, SQL, Spark, and PySpark. Builds batch, streaming, and production ML pipelines; manages Delta Lake, Unity Catalog, Workflows, MLflow, governance, data quality, observability, and model deployment. Partners with data scientists and business teams, optimizes performance and costs, contributes to platform architecture, mentors engineers, and documents operational practices.

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, 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, 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.

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.

Support electrical design, engineering, commissioning, and delivery of liquid-cooled data centre solutions: prepare layouts, SLDs, wiring diagrams, BOMs, perform load calculations, assist testing/FAT/SAT, troubleshoot commissioning issues, coordinate cross-functional teams, and ensure compliance with IEC/NEC/IEEE and company HSE standards.

Design, implement, and lead large-scale generative AI and ML systems (LLM fine-tuning, RAG, agents, Graph ML). Build REST APIs, deploy with Docker/Kubernetes, optimize big-data workflows, mentor engineers, and operate ML services on cloud platforms.