Top Data Engineer Jobs in Delhi

Design, develop, and maintain Snowflake-based data solutions and ETL pipelines. Ensure data quality, optimize pipeline performance, document workflows, and collaborate with teams while mentoring juniors.

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

As a Data Engineer, you will enhance data pipelines, support ML workflows, and improve cloud infrastructure on Azure and AWS, collaborating with Scientists and Engineers to turn data into actionable insights.

Design, build, and maintain Microsoft Fabric data architecture and ETL pipelines using Fabric, Synapse, or Azure Data Factory. Transform and optimize data for analytics and AI, support RAG/LLM workloads, ensure data quality, governance, and document processes.

Build and maintain scalable Azure and Databricks ETL/ELT pipelines using SQL and Python/PySpark. Design cloud-native data lakes, ingest/process data with ADF/ADLS, optimize data models, monitor pipeline health, troubleshoot failures, and collaborate with architects, QA, DevOps, and business teams.

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.

The Data Engineer will design, develop, and maintain ETL/ELT data pipelines, leveraging PySpark and Databricks to process large datasets, ensuring data quality, reliability, and performance optimization.

Build and deploy ETL/ELT pipelines on GCP to integrate third-party media data at scale. Use Python, SQL, dbt and BigQuery for data modeling and transformations, manage pipeline code with Git, apply IaC (Terraform), explore streaming (Kafka/Pub/Sub), and implement CI/CD for reliable production data workflows.

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, build, and maintain scalable cloud-based ETL/ELT data pipelines (Python, PySpark, SQL, Snowflake) supporting P&C insurance pricing, rating, underwriting, and actuarial analytics. Collaborate with actuaries, data scientists, analysts, and cloud engineers to deliver reusable data frameworks, batch/incremental ingestion, data quality, and transformations for GLM and predictive models.

Lead and deliver end-to-end AWS data engineering solutions: design, build, and optimize data pipelines, lakes, and warehouses; implement serverless and streaming architectures; mentor engineers; ensure quality, scalability, cost-efficiency, and CI/CD for enterprise data platforms.

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.

Design, build, and maintain scalable batch and streaming ETL/ELT pipelines, production data services (REST/gRPC), and cloud-based data infrastructure. Implement CDC and time-travel data architectures, ensure data quality with tests and CI/CD, optimize data models for OLTP/OLAP, and collaborate cross-functionally to deliver reliable production data systems.

Responsible for designing and developing a GCP data ecosystem, creating data pipelines, optimizing data architectures, troubleshooting issues, and mentoring junior engineers.

Build and maintain scalable Azure cloud data platforms and ETL/ELT pipelines using Databricks, Synapse, ADF, ADLS. Develop SQL and Python/PySpark transformations, monitor pipeline health, troubleshoot failures, and collaborate with architects, QA, DevOps, and business stakeholders.

As a Data Engineer, you'll develop data pipelines, automate processes, and collaborate with teams to enhance data management and analytics for national security. You'll analyze raw data, manage data platforms, and engage with clients.

The Senior Associate Data Engineer develops structured datasets for analysis, manages ETL processes, and designs data models to support business analytics and reporting.

The role involves designing and building data infrastructure, developing ETL/ELT applications, collaborating with teams, and improving engineering processes for analytics and AI capabilities.

Principal Data Engineer responsible for building big-data solutions using PySpark and Databricks, writing and optimizing SQL, developing Python pipelines, and working with Hadoop/Hive, Git, Airflow/Dagster, and Azure.

Design, build, and maintain scalable ETL/ELT pipelines and data warehouses using Snowflake, AWS Glue, Python, and SQL. Optimize performance, implement CI/CD with Azure DevOps, ensure data quality and governance, collaborate with analytics teams, and troubleshoot pipeline issues.

As a Data Engineer, you will develop and manage data pipelines, ensuring reliable data ingestion and delivery while optimizing for simplicity and performance.