Top Data Engineer Jobs in Delhi

Design, develop, and optimize secure, scalable cloud data platforms and ETL/ELT pipelines across AWS, Azure, and GCP. Write Python and SQL, implement data governance and quality controls, optimize performance and costs, and support analytics and BI layers. Contribute to architecture, pre-sales demos, proofs of concept, and client discussions. Mentor junior developers, conduct code reviews, and collaborate with architects, QA, DevOps, and business stakeholders.

Designs and develops scalable cloud data platforms and ETL/ELT pipelines using Python and SQL. Optimizes performance and costs, implements governance, security, quality, and compliance controls, and supports analytics consumption. Collaborates with architects and stakeholders, contributes to pre-sales prototypes and demos, mentors junior developers, conducts code reviews, and recommends improvements across AWS, Azure, and GCP environments.

Maintains, cleans, manipulates, and secures operational and analytics databases. Designs scalable data pipeline architecture, builds complex data sets and analytics tools, automates manual processes, improves infrastructure, troubleshoots database issues, and supports software engineering, data science, analytics, product, design, and executive stakeholders.

Maintains, cleans, manipulates, and secures operational and analytics data. Designs scalable data pipeline architectures, builds complex data sets and analytics tools, automates manual processes, improves infrastructure, troubleshoots database issues, and supports data, product, design, analytics, and data science teams with technical data needs.

Build and maintain cloud data pipelines, transformations, orchestration workflows, governance controls, and analytics-ready datasets. Integrate APIs, Databricks, BI tools, and platform connectors while troubleshooting data quality and performance issues. Collaborate with product, engineering, DevOps, security, and client-facing teams on architecture, access management, infrastructure migrations, documentation, and production support.

Build and maintain cloud data pipelines, transformation workflows, orchestration, governance, and access controls for analytics products. Integrate APIs, Databricks, BI tools, and multi-tenant data platforms while troubleshooting quality and performance issues. Collaborate with product, engineering, DevOps, security, and client-facing teams on platform features, architecture documentation, infrastructure migrations, and production support.

Manage, validate, migrate, and QA/QC environmental data in EQuIS and related systems; configure workflows, build Power BI dashboards, support reporting, automate processes, and prepare documentation and SOPs to ensure data quality and compliance across global projects.

Build and maintain scalable GCP-based ETL/ELT pipelines using Node.js and Python. Integrate external APIs, develop event-driven workflows with Pub/Sub and Eventarc, and automate deployment through Cloud Build and CI/CD. Optimize data processing and storage costs, apply data modeling and warehousing principles, and ensure security and governance. Collaborate with cross-functional teams to translate business requirements into maintainable data solutions.

Build and maintain scalable GCP-based ETL/ELT data pipelines using Node.js and Python. Integrate external APIs, develop event-driven workflows with Pub/Sub and Eventarc, and optimize BigQuery data processing and storage. Automate deployment and testing through Cloud Build and CI/CD, while ensuring data security, governance, and compliance. Collaborate with cross-functional stakeholders to translate business requirements into maintainable data solutions.

Support, maintain, troubleshoot, and optimize production Databricks applications, Spark jobs, SQL queries, Delta Lake pipelines, clusters, workflows, and workspace configurations. Implement data quality checks, logging, alerting, governance, security, and cost-management practices. Collaborate on enhancements and bug fixes, reduce technical debt, document operational processes, and participate in EST-aligned support rotations.

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 scalable data products using Snowflake, dbt, and Airflow. Responsibilities include dimensional data modeling, SQL transformations, pipeline orchestration, data quality testing, exploratory analysis, SLA monitoring, documentation, and stakeholder requirement gathering. Partner with analytics, product, engineering, and business teams to deliver reliable, production-ready datasets while following software engineering, CI/CD, testing, and deployment practices.

Build and maintain scalable GCP-based ETL/ELT pipelines using Python, BigQuery, and Google Cloud Storage. Support machine learning infrastructure through feature stores, model data feeds, Vertex AI integration, and MLOps workflows. Ingest data from APIs, streaming platforms, and databases; optimize SQL and BigQuery architecture; implement CI/CD, testing, data quality checks, monitoring, and governance. Collaborate with data scientists, ML engineers, and product managers to operationalize production machine learning systems.

Leads data engineering initiatives involving ETL/ELT pipelines, database optimization, orchestration, big data technologies, data modeling, BI performance, and data quality monitoring. Requires strong Scala, Python, SQL, and shell scripting skills, plus payments-domain expertise covering transaction processing, settlements, disputes, reconciliation, gateways, acquiring, issuing, and PCI-DSS compliance. The role also involves cross-functional communication, planning, facilitation, negotiation, and collaboration with senior stakeholders and clients.

Design, build, and optimize end-to-end data solutions using Microsoft Fabric, Azure Synapse, Databricks, Azure Data Factory, and Power BI. Develop scalable real-time and batch pipelines, data models, semantic and dimensional models, dashboards, reports, and self-service analytics. Apply medallion architecture, DataOps, monitoring, security, governance, quality controls, and performance tuning. Integrate Azure Machine Learning and streaming technologies when needed while delivering enterprise data and BI solutions for clients.

Designs and maintains SAP-integrated ETL/ELT pipelines connecting ECC, S/4HANA, BW/4HANA, and cloud data platforms. Responsibilities include SAP data extraction, transformation, real-time integrations, dimensional modeling, database optimization, data quality monitoring, alerting, cost optimization, and documentation. The role partners with functional consultants, BI teams, FP&A stakeholders, and business leaders while developing cloud skills across AWS services such as Glue, Lambda, Airflow, and Redshift.

Designs and manages Finance data integrations and migrations into Oracle Cloud. Configures Oracle Fusion extraction, FDI pipelines, semantic models, data catalogs, dashboards, and reporting. Leads technical roadmaps, data-quality validation, monitoring, reconciliation, testing, deployments, and production support. Requires deep Oracle data engineering expertise and strong understanding of Finance, SCM, and HCM processes, with the ability to advise business stakeholders.

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 implement governed finance data products using data warehouses, data mesh architectures, star schemas, facts, and dimensions. Develop advanced BI reports, dashboards, visualizations, and analytics narratives across tools such as Power BI, Looker, and Tableau. Apply agentic AI, semantic layers, and RAG to enterprise data for contextualized insights. Support finance, accounting, FP&A, and controller functions, with potential work involving NetSuite, Boomi, or Salesforce data platforms.

Design, build, and maintain enterprise ETL/ELT pipelines in Microsoft Fabric; ingest and transform data from diverse sources into dimensional star schemas; optimize performance and cost; implement data quality checks and monitoring; document architecture and pipelines; collaborate with analysts, stakeholders, and global engineering teams to deliver Power BI-ready analytics datasets.

As a Senior Data Engineer, you will develop and maintain data pipelines for healthcare data, ensuring data quality, reliability, and adherence to enterprise standards while collaborating with architects and stakeholders.

The Senior Data Engineer will develop data ingestion, transformation, and enrichment pipelines, ensuring data quality and reliability. Responsibilities include creating Bronze and Silver data layers, using SQL and dbt for implementation, and collaborating with architects and stakeholders to align data solutions with enterprise standards.

Build, optimize, and maintain scalable AWS-based data pipelines and data lakes (Snowflake/Databricks). Implement streaming ingestion (Kinesis/Kafka), CDC, automated data quality checks, and ML-ready feature stores. Collaborate with Product, ML, and Analytics teams, own pipeline QA, observability, and participate in on-call rotations to resolve production data issues.

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 optimize scalable GCP data pipelines and BigQuery data warehouses. Develop ETL/ELT workflows, implement data modeling, ensure data quality, optimize query performance and costs, maintain orchestration with Cloud Composer/Dataflow/Pub‑Sub, and automate deployments with CI/CD while enforcing security and governance.