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 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.

Design, develop, and maintain scalable Snowflake data pipelines and ETL/ELT processes. Ensure data quality, performance tuning, and security. Collaborate with architects, data engineers, and business teams; contribute to CoE guidelines, POVs/POCs, and cross-project reference architectures. Support delivery teams and leverage Snowflake features (Snowpipe, Streams, Tasks, Stored Procedures) for analytics and reporting.

Design and implement scalable, secure Snowflake architectures and data pipelines. Lead data ingestion, ETL, transformations, performance tuning, security controls, and best practices. Mentor teams, support presales, run POCs, and standardize data modeling across projects using Snowflake features and integration tools like dbt and Fivetran.

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.

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.

The Data Engineer will build and optimize data pipelines, using SQL and Python while working collaboratively to support business analytics and AI initiatives.

Deploy, configure, and operate Rhombus Power's AI platform across cloud, on-premises, and edge environments. Install and validate hardware, networking, and compute infrastructure; support AWS, Docker, containerized applications, CI/CD, monitoring, and troubleshooting. Perform site readiness, rack mounting, backups, UAT, and disaster recovery. Act as on-site technical liaison, maintain deployment documentation, escalate issues, and provide ongoing operational support for defense customers.

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.

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.

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

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

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.

Design, build, and maintain production data pipelines and integrations across applications, databases, and cloud services. Support data architecture, ingestion, embedding workflows for AI, data quality, monitoring, and platform reliability. Collaborate with Full Stack and AI teams to deploy scalable cloud-based data infrastructure and establish data engineering standards and best practices.

Design and develop data-driven solutions and scalable data pipelines using Python and SQL. Leverage Azure data products to build robust, secure architectures, ensuring data integrity, performance, and governance while supporting data-centric client projects.

Design, build, and maintain scalable ETL/data pipelines and data management blueprints. Ingest, transform, and curate large complex datasets for analytics, support cloud (AWS) implementations, enable downstream consumers, and provide technical leadership and operational support to ensure data governance, quality, and automation.

Design and develop scalable data-driven solutions and pipelines using Python and SQL. Leverage Azure data services to build secure, high-performance architectures, ensuring data integrity, governance, and overall success of data projects.

Design and build scalable data pipelines and architectures on Azure using ADF, Databricks, and Synapse. Implement Kimball/Data Vault modeling, write ETL in Python and T-SQL, ensure data governance (Azure Purview), automate tests, troubleshoot issues, document processes, and collaborate across teams.

As a hands-on data engineer, you'll build and enhance scalable data platforms, develop data pipelines, and collaborate with various teams to improve data solutions and system performance.

Lead technical direction for data platforms, design and maintain data pipelines for reporting and AI (RAG, embeddings, vector stores), reduce architectural debt, mentor engineers, partner with product leadership, and represent Mitratech externally.

Design, build, and maintain scalable ETL pipelines and data models for analytics and ML. Ensure data quality, optimize performance, enforce governance and security, and collaborate with stakeholders to deliver reliable, production-ready data for reporting and data science.

Design, build, and maintain cloud data pipelines and architectures using SQL, Python, Snowflake, and AWS. Implement CI/CD for data workflows, use developer assistance tools, and collaborate across teams to deliver reliable data solutions.

Design, develop, and coordinate cloud data solutions using Snowflake, Python, and AWS. Support product releases, deployments, testing, and post-install support. Collaborate with architects and tech leads, troubleshoot issues, and maintain documentation. Contribute to agile squads, adopt new technologies, and enable Fidelity Wealth business data needs.

Perform data migration/refresh from production, support Python-based data refresh framework, run post-migration fixes, validate and reconcile data, monitor and resolve batch jobs and pipeline issues, perform performance checks, and coordinate with dependent teams.

Design, build, and maintain scalable ELT/data pipelines and connectors using Airflow, Python, and PySpark. Implement DataOps and CI/CD for data workflows, monitor and troubleshoot pipelines, validate data quality, collaborate with cross-functional teams, and document data processes and governance.