Top Data Engineer Jobs in Delhi

Design, build, and maintain scalable data pipelines and ETL/ELT workflows using Azure Data Factory, Databricks, PySpark, Python, and SQL. Transform large datasets, optimize data ingestion and queries, monitor pipeline performance, resolve data quality issues, support data modeling, document engineering standards, and contribute to cloud data infrastructure improvements. The role also involves collaboration with architects and senior engineers and requires familiarity with Azure Synapse, Delta Lake, governance, and security.

Lead backend and data-platform architecture while remaining hands-on with Java production code. Design and operate distributed systems for event ingestion, analytical querying, data processing, and low-latency serving. Work with ClickHouse, BigQuery, Bigtable, Kafka, Redis, GCP, and Dataflow to solve scalability, reliability, performance, and data-correctness challenges. Set technical direction, review designs, mentor engineers, and guide platform convergence across Wingify and AB Tasty.

Develop robotics datasets and evaluations for embodied AI systems. Responsibilities include defining data schemas, annotations, quality standards, collection protocols, validation tools, and review workflows. The role also involves running experiments to assess how data quality and structure affect model performance, collaborating with research, engineering, vendors, and customers, and taking research tools from conception through deployment.

Develop a Java-based desktop IDE, coordination server, and relational data backend for a semiconductor test engineering DSL. Responsibilities include architecture, data modeling, versioning, synchronization, language editing and validation, debugging, automated testing, and platform quality. Collaborate with international engineering teams and domain experts to build a reliable, extensible development environment.

Develop a Java-based desktop IDE, coordination server, and relational data backend for a semiconductor test engineering DSL. Responsibilities include desktop UI development, data modeling, client-server synchronization, language editing and validation, architecture, debugging, automated testing, and platform quality. The role involves shaping a modular development environment and collaborating with international engineering teams and domain experts.

Principal Data Engineer responsible for developing and supporting Databricks pipelines, integrating heterogeneous healthcare data sources, modeling unified data products, and ensuring data quality and availability. The role partners with business stakeholders, product teams, data engineers, and clinical leaders to gather requirements, create datasets, support ad hoc analytics, enable machine learning and AI use cases, and assist with acquisition integrations. Extensive healthcare data, cloud, SQL, PySpark, and Python experience is required.

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.

Designs and operates GCP or AWS infrastructure for AI, geospatial, and data-intensive workloads. Builds CI/CD pipelines, manages Docker and Kubernetes environments, supports ML training and inference, optimizes cloud costs, and maintains data pipeline infrastructure. Establishes monitoring, logging, alerting, IAM, secrets management, vulnerability scanning, and network controls. Supports API platforms and collaborates with AI, geospatial, and product teams to resolve deployment, scaling, and reliability issues.

Build and support fund accounting data solutions for asset management, including integrations, transformations, reconciliations, controls, and finance reporting products. Develop large-scale data pipelines using Databricks, Python, PySpark, and SQL; work with investment data, NAV calculations, accruals, corporate actions, fees, and general ledger information. Support cloud-based delivery, data quality, orchestration, CI/CD, and direct collaboration with US stakeholders.

Operate and improve production data infrastructure across large-scale distributed cloud systems. Monitor performance, manage alerts, respond to incidents, perform root cause analysis, automate repetitive tasks, support CI/CD pipelines, document runbooks, and assist with capacity planning. The role requires participation in an on-call rotation and collaboration with Engineering to maintain reliability, efficiency, and uptime.

Provides first-line production support for enterprise applications by monitoring Splunk and New Relic dashboards, investigating alerts, triaging incidents, managing tickets, documenting evidence, escalating issues, and supporting maintenance and deployment windows. The role requires monitoring knowledge, incident management skills, clear communication, and the ability to handle multiple production incidents. Familiarity with AWS data platforms and Python, SQL, Scala, or Shell is preferred.

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.

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

Designs, develops, and maintains scalable Azure data infrastructure, pipelines, storage layers, and integrations. Provides technical leadership, supports data literacy, collaborates with analysts and stakeholders on data solutions, and oversees external consultants. The role promotes platform adoption, documentation, governance, security, privacy, data quality, and access controls while driving continuous improvement. It also develops optimized data models and automated workflows using SQL, Python or PySpark, and infrastructure-as-code tools.

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.

Design and build scalable data and AI infrastructure for production LLM applications. Responsibilities include developing RAG systems, embedding and vector retrieval pipelines, AI agents, LLM evaluation and observability frameworks, and batch or streaming data platforms using Databricks, Spark, Snowflake, Delta Lake, and Airflow. The role also involves optimizing distributed workloads, establishing data governance and quality practices, and partnering with engineering and product teams to productionize AI capabilities.

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.

Designs and leads enterprise business intelligence and analytics solutions using Power BI, Microsoft Fabric, Snowflake, Azure Data Factory, and modern data architectures. Responsibilities include developing semantic models, datasets, dashboards, data pipelines, data products, and scalable data platforms; implementing governance, quality, lineage, and metadata practices; enabling self-service analytics; optimizing performance; evaluating emerging tools such as Promethium; and advising stakeholders while mentoring data teams.

Designs and maintains enterprise data architecture, including conceptual, logical, and physical data models. Establishes data standards, metadata conventions, governance policies, security frameworks, and data dictionaries. Leads Master Data Management initiatives to improve consistency and integrity across platforms. Collaborates with business, analytics, application, and engineering teams, while supporting database, data warehouse, integration, and ETL pipeline efforts aligned with business, regulatory, security, and performance requirements.

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.

Build, monitor, optimize, and maintain ETL/ELT pipelines using Google Cloud technologies. Develop complex SQL transformations, design dimensional data models, improve query performance and cloud cost efficiency, and ensure data integrity. Collaborate with analysts and stakeholders to translate SaaS and insurance metrics into technical solutions. Participate in code reviews, documentation, troubleshooting, and data warehouse lifecycle management while supporting US business hours.

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 pipelines and medallion-schema data architectures. Ensure data quality, perform cleaning and migration, integrate varied sources, and support reporting and analytics in collaboration with stakeholders.

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.