Top Data Engineer Jobs in Delhi

Design, build, and optimize Snowflake data models and dbt pipelines. Create Power BI reports and paginated RDLs with DAX and row-level security. Manage CI/CD with Azure DevOps, version control with Git, enforce data quality and governance, and optimize warehouse cost and performance while collaborating with business stakeholders.

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.

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.

As a Data Engineer, you will build scalable ETL/ELT pipelines, optimize data structures, and develop impactful Power BI solutions for stakeholders.

The Data Engineer will design and maintain data models, develop BI solutions, integrate data, and optimize data flows while collaborating with stakeholders.

As a Data Engineer, you will analyze and enrich structured and unstructured data, enhance data quality, and develop insights for financial strategies.

Design, build, and maintain scalable cloud-native data pipelines for trade lifecycle processing. Ingest, transform, and normalize high-volume financial data (DTCC, ISO15022), implement MQ/API-based ingestion, optimize SQL Server performance, maintain UTR schemas and trade exception databases, ensure data quality, lineage and governance, and collaborate with reconciliation, settlements, and operations stakeholders.

Design and build a greenfield, multi-layer data warehouse and semantic metrics layer; separate analytics from OLTP; ensure data quality, traceability, tenant isolation, and high SLAs; enable linking of structured records to drone imagery/video; deliver self-service data access, observability, and infrastructure automation to scale across products and tenants.

Build and maintain end-to-end data pipelines using PySpark and SQL on Databricks/Delta. Establish data modeling and pipeline best practices, create AI-ready analytical datasets, troubleshoot pipeline issues, and collaborate with stakeholders to embed business logic and optimize reliability, efficiency, and performance.

The Engineering Manager leads the Data Engineering team, overseeing the design of scalable data pipelines using AWS technologies, mentoring engineers, and collaborating with stakeholders on data-first initiatives.

The Senior Data Engineer is responsible for developing Python applications, optimizing data pipelines, integrating APIs, and collaborating with teams to deliver data solutions.

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, build, and maintain centralized data pipelines, warehouses, lakes and hubs. Assemble complex datasets, implement ETL processes, build analytics tools and reports, support cross-functional teams, ensure secure cross-region data separation, and create product data tools to enable insights on acquisition and operational metrics.

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

Build and maintain scalable batch and real-time data pipelines using Kafka, Maxwell, Spark and dbt; design medallion-based data models; implement cloud-native data platforms (S3, Trino, BigQuery); optimize performance, ensure data quality, observability, and SLA adherence; enable analytics through trusted datasets and Metabase dashboards.

Design, build, and operate scalable data engineering services and ingestion pipelines (CDC/streaming). Develop and optimize a centralized analytics warehouse, data models, and aggregation strategies. Build APIs, enforce multi-tenant security and data governance, monitor pipeline health, improve automation and observability, and ensure production reliability through strong engineering practices.

Lead a two-month project to assess the current data environment and design, build, and optimize a scalable cloud data warehouse. Deliver automated ETL/ELT pipelines, dimensional models, analytics-ready datasets, documentation, and handover while partnering with analysts and stakeholders for BI and reporting.

Design, build, and operationalize AI-enabled data pipelines and production-grade AI applications using Python and Snowflake. Prepare and transform large datasets, develop features and embeddings, support deployment and monitoring of ML/LLM models, optimize Snowflake for AI workloads, and collaborate with data science and engineering teams to deliver scalable APIs and RAG-enabled data workflows.

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 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.

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.

We are looking for a Data Engineer with skills in Python, SQL, and Azure technologies to develop Big Data applications. Responsibilities include working with Spark and Docker to handle large scale data processing and analytics.

The Azure Data Engineer will build data pipelines and dataflows, implement security modules, and work with Azure services while supporting business requirements and data analysis.

The Data Engineer role involves analyzing raw data, building data systems, conducting complex analyses, and enhancing data quality by collaborating with data scientists and architects.