Top Data Engineer Jobs in Delhi

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.

Build scalable data pipelines and ETL/ELT processes using Python and modern data platforms. Perform exploratory analysis, validate data, develop data models, and support analytics and real-time insights. Collaborate with data scientists, analysts, engineers, and product teams while delivering tested, reliable features. The role also contributes to data quality policies, processes, scorecards, and product operations reporting.

Build scalable data pipelines and ETL/ELT processes using modern data platforms. Perform exploratory analysis, validate data, develop models supporting analytics and real-time insights, and collaborate with data science, product, engineering, and customer success teams. Write tested Python code, contribute to data quality initiatives, and deliver end-to-end features in an agile environment.

Build and operate production-grade data infrastructure, including lakehouse architecture, ETL/ELT pipelines, data warehouses, data marts, migrations, and governed data products. The role uses Microsoft Fabric, Azure, Databricks, Spark, Python, and SQL to support reporting, traceability, compliance, impact measurement, and AI/ML use cases. Responsibilities also include data quality, metadata, lineage, security, access controls, CI/CD, and platform optimization across complex international systems.

Extract and maintain support data, build pipelines and dashboards, analyze ticket trends and customer journeys, identify automation opportunities, and translate insights into prioritized improvements. The role implements Zendesk workflows and APIs, supports AI and automation initiatives, runs experiments, measures business impact, and communicates findings to technical and non-technical stakeholders.

Own end-to-end data engineering and BI delivery, including scalable ETL/ELT pipelines, data quality, Power BI semantic models, reports, dashboards, and recurring data processes. Maintain Synapse and Power BI tooling, support production incidents, ensure governance and reliability, and partner with stakeholders on trusted datasets. Enable self-service analytics and natural-language data access through AI. The role requires strong SQL, Azure, Power BI, and regulated-industry experience.

Design and deliver cloud-native data platforms, scalable big data pipelines, lakehouses, warehouses, transformation frameworks, BI solutions, and ML-enabled data workflows. Lead client-facing pre-sales engagements, technical workshops, proof-of-concepts, executive conversations, and enterprise data modernization initiatives. Build reusable architectural IP, influence senior stakeholders, and translate ambiguous business challenges into production-ready solutions using AWS and modern data technologies.

Design and maintain batch and real-time data pipelines, data lakes, warehouses, marts, and reusable dbt models using Spark, Kafka, Maxwell, S3, Trino, BigQuery, and related technologies. Optimize distributed processing, data models, queries, storage, reliability, and costs. Establish data quality, observability, governance, automation, and CI/CD practices while partnering with Product, Engineering, Analytics, Data Science, and business stakeholders.

Designs, builds, deploys, and supports secure, scalable cloud data solutions and pipelines for enterprise clients. Translates business requirements into data architectures, integration patterns, and models; ensures performance, reliability, governance, and documentation. Provides technical leadership during reviews and workshops, collaborates with engineering and analytics teams, contributes reusable accelerators and best practices, mentors colleagues, and manages client engagements through delivery.

Build, monitor, optimize, and maintain cloud-based ETL/ELT pipelines supporting business intelligence and reporting. Develop high-performance SQL transformations, manage data warehouse schemas, tune query and pipeline performance, and optimize cloud costs. Collaborate with analysts, data scientists, actuaries, and architects to translate insurance and SaaS requirements into scalable technical solutions. Participate in code reviews, documentation, troubleshooting, and data integrity initiatives.

Build production-grade agentic AI systems that reason, search, and execute workflows. Develop RAG pipelines, AI agents, semantic search layers, data processing solutions, dimensional data models, and scalable AI workloads. Work with structured and unstructured data, LLMs, vector databases, cloud infrastructure, CI/CD, and ML observability. Responsibilities include fine-tuning models, prompt engineering, optimizing context windows, and deploying reliable AI applications.

Develop, optimize, and maintain scalable ETL/ELT data pipelines for structured and unstructured data using Python and PySpark. Ensure data quality through validation, cleansing, transformation, and reconciliation. Collaborate with technical and business stakeholders, troubleshoot performance issues, conduct code reviews, and implement coding standards and CI/CD pipelines. Work with big data frameworks, cloud platforms, databases, data models, schemas, and warehousing solutions.

Design, develop, and maintain scalable Microsoft Fabric and Azure data solutions. Build Lakehouse, Data Warehouse, OneLake, ETL/ELT pipelines, data transformations, and integrations using Python, PySpark, SQL, and Fabric tools. Implement medallion architecture, optimize data models and workloads, establish data quality monitoring, and support CI/CD deployments. Collaborate with analytics and business teams to deliver enterprise-scale data platforms.

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 batch and streaming data pipelines across AWS, Azure, and GCP using Databricks, Spark, PySpark, SQL, and Delta Lake. Implement medallion architecture, CDC, SCD, data quality, governance, security, metadata, and lineage controls. Configure storage, orchestration, CI/CD, monitoring, troubleshooting, and performance optimization. Collaborate with architects, data scientists, engineers, analysts, and product teams while documenting pipelines, transformations, testing, and operational procedures.

Build and optimize ETL/ELT pipelines feeding BigQuery, audit and troubleshoot data quality, and create reliable Looker Studio dashboards. The role supports sales leaders by turning raw data into accurate, actionable insights, improving reporting performance, eliminating manual processes, and ensuring data integrity across Curvion’s revenue operations ecosystem.

Design, build, and maintain scalable data pipelines and ETL/ELT solutions using Microsoft Azure Fabric, Databricks, and Azure data services. Translate business requirements into technical solutions, optimize data workflows for performance and cost, troubleshoot production issues, and collaborate with architects, analysts, developers, and global stakeholders. Use AI-assisted development and automation to improve engineering productivity while owning delivery quality, timelines, and outcomes.

Build full-stack tools, backend services, APIs, dashboards, and data workflows for assessing and improving AI training data and evaluations. Partner with quality and research teams to inspect agent trajectories, validate graders, measure quality, investigate workflow issues, and turn internal prototypes into reliable production platform capabilities.

Design and maintain analytical pipelines and reporting solutions using BigQuery and MariaDB. Develop Python and FastAPI tooling, APIs, and automation; write advanced SQL; apply AI/ML capabilities; document data architecture and lineage; and translate complex analysis into actionable business insights. Collaborate across departments on process improvements and independently own analytics projects end to end.

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.