Top Data Engineer Jobs in Delhi

Design, build, and maintain cloud-native data pipelines and ETL/ELT workflows for high-volume trade lifecycle data. Integrate DTCC sources (RTTM, Settlement Web) into a Unified Trade Record, process ISO 15022 messages, manage trade exception databases, optimize SQL Server performance, and ensure data quality, lineage, governance, and observability while collaborating with business stakeholders.

Design, build, and maintain scalable Snowflake data pipelines and ELT/ETL workflows. Optimize Snowflake performance, manage ingestion from databases, APIs and files, implement data models (star schema), support BI consumers, and maintain pipeline health and documentation.

Design, document, and implement data pipelines and transformations (dbt, Airflow) feeding Snowflake warehouses. Ensure data correctness, monitor/triage incidents, optimize SQL performance, apply SCD/CDC/DQ frameworks, collaborate with product owners and stakeholders, and maintain governance, security, and version-controlled code.

The Google Cloud Data Engineer will design and implement data pipelines using Google Cloud technologies and collaborate with analysts to ensure data quality and governance.

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 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.

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

Design and build scalable ELT pipelines and data models using Databricks, dbt, Airflow and BI tools. Lead data governance, observability, and self-serve analytics initiatives; partner with data scientists, product and business stakeholders; mentor engineers and drive enterprise-wide data platform and architecture decisions to enable reliable, governed, and AI-enabled data products.

Design, build, and maintain scalable Azure-based data pipelines and systems (Databricks, Data Lake, Synapse, ADF). Collaborate with cross-functional teams, ensure data quality and integrity, optimize performance, monitor and troubleshoot systems, and stay current with industry trends to improve solutions.

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.

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.

Design, build, and maintain Python-based applications and ETL pipelines to extract, transform, and load data. Integrate APIs and web scraping, write optimized SQL for relational databases, implement data processing algorithms, and collaborate with teams to deliver scalable, high-performance solutions while ensuring code quality and best practices.

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.

Maintain and monitor Python-based web scraping configurations, detect and resolve scraping issues, coordinate with stakeholders, produce reports, and build ETL pipelines to ingest and transform data into the data lake.

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.

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.

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

This role involves collaborating with users to build data solutions, managing AWS data technologies, and automating workflows for efficiency.

The Azure Data Engineer will implement big data pipelines using Azure technologies, collaborate with stakeholders, and utilize tools like Data Factory and Databricks.