Top Data Engineer Jobs in Delhi

Own the architecture, scalability, governance, and technical direction of Amtech’s cloud data platform, including warehouses, tenant data stores, feature stores, and event pipelines. Lead multi-tenant data architecture, hybrid AI data routing, data security, observability, and reliability standards. Build data foundations for analytics, machine learning, and AI agents, including vector databases and RAG pipelines. Provide technical leadership through design reviews, mentoring, roadmap planning, and cross-functional architecture collaboration.

Migrate SQL Server data products and analytical workloads to a modern cloud platform. Redesign legacy SQL, ETL processes, and business logic; build scalable pipelines and analytical models; validate data quality; collaborate with stakeholders; and improve platform reliability, performance, cost efficiency, and AI-assisted data engineering automation.

The role involves building and scaling the Acceldata Open Data Platform, focusing on observability, coding, debugging, and collaborating with teams and the open-source community.

Build and enhance enterprise data pipelines into Databricks Unity Catalog and Delta Lake. Improve remediation and entity-matching layers (including D&B DataBlocks), integrate APIs, and support LLM-assisted remediation and CMG redesign projects.

Design and implement distributed data systems for Apache Hudi and Onehouse’s managed data lakehouse. Responsibilities include developing concurrency controls, transactional capabilities, indexing schemes, metadata and data-access systems, and optimization algorithms for Kubernetes-based distributed processing. The role involves analyzing complex data, prototyping performance improvements, deploying solutions safely to production, and serving as a subject matter expert in databases, query engines, storage, and stream processing.

Build and productionize distributed data infrastructure for Onehouse’s managed lakehouse platform. Responsibilities include developing Apache Hudi transactional capabilities, concurrency control, indexing, metadata and data-access systems, and optimization algorithms for distributed processing on Kubernetes. The role involves analyzing complex data, prototyping performance improvements, collaborating with engineers, and safely deploying solutions. Candidates should bring strong object-oriented programming, distributed systems, algorithm, and relational database expertise.

Design, deploy, and maintain AWS and Databricks data infrastructure, data lakes, and scalable ETL pipelines. Build custom ingestion services and connectors using Airbyte, Go, and Python; optimize performance, reliability, monitoring, and data quality. Implement cloud data governance, security, access controls, encryption, and compliance practices while collaborating with platform engineers, analysts, and stakeholders.

The Data Engineer role involves designing and maintaining ETL pipelines, optimizing data storage solutions, collaborating with teams, and ensuring data accuracy and integrity.

Designs, builds, and maintains ETL/ELT pipelines feeding Microsoft Fabric Lakehouse platforms. Responsibilities include data ingestion, bronze/silver/gold layer development, CDC, incremental loading, data quality, metadata governance, performance tuning, monitoring, automation, and low-level design documentation. The role collaborates with architects, data modelers, and business analysts to deliver reliable, reusable datasets and promote self-service analytics while applying security, resilience, compliance, and cost-optimization standards.

Design, build, and optimize large-scale ETL pipelines and distributed data applications (batch and streaming) using Spark and Scala. Ensure data quality and performance, deploy and manage workloads in cloud/container environments, troubleshoot production issues, and collaborate with product, engineering, and analytics teams to deliver scalable AdTech data solutions.

Design, build, and operate scalable batch and real-time data pipelines and analytics platforms. Partner with enterprise customers to define requirements, own technical delivery from discovery to adoption, and present architectures to stakeholders. Drive data governance, observability, CI/CD, and platform optimization while mentoring junior engineers and collaborating with Product, Engineering, and Customer Success teams to enable analytics and AI/ML applications.

Triage and resolve Master Data Management (Party, Risk, Reference) issues in Stibo STEP, run targeted data-cleansing sprints, monitor data quality dashboards, support releases/UAT, and maintain documentation and playbooks to prevent recurring data issues.

Lead HUD's data quality strategy for RL training data: build QC systems, validate synthetic data at scale, design evaluation experiments, partner with researchers and vendors, convert research insights into production validation pipelines and tools, and mentor research engineers.

Design and operate governed, AI-ready data products, semantic layers, and natural-language analytics experiences on Snowflake and/or Databricks. Build ETL/ELT pipelines with dbt, Airflow, Snowpark, and PySpark; deploy Cortex or Genie capabilities; develop RAG applications; implement governance, security, lineage, and metadata controls; optimize platform and LLM costs; and partner with Finance stakeholders on trusted data products.

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.

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.

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.

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.

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.