Top Data Engineer Jobs in Delhi

Design and build data services and pipelines supporting machine learning products. Develop automation tools for model deployment, maintain production data systems, improve infrastructure, participate in code reviews, and collaborate across engineering, data science, and product teams. The role requires expertise in distributed systems, large-scale data processing, CI/CD, container orchestration, and AI-enabled workflow improvements.

Execute large-scale data platform migrations to Snowflake, adapting approximately 1,000 datasets and existing pipelines. Build, test, monitor, and troubleshoot reliable Apache Airflow workflows while supporting migrations from Airflow 2 to 3, Confluent regions, and legacy formats to Iceberg. Apply Python, advanced SQL, Spark, PySpark, Kafka, AWS S3, and Snowflake data modeling to ensure data quality, reliability, and timely delivery.

Designs and develops scalable data management solutions, including data acquisition, integration, transformation pipelines, data structures, and cloud or edge infrastructure. Uses Azure Data Factory, ADLS, Databricks, SAP Data Services, DevOps pipelines, and containerization. Collaborates with analysts, architects, product owners, and technical leads to define requirements, deliver high-quality code, and support application enhancements.

Build and maintain FinOps data pipelines, BigQuery and dbt models, cost attribution systems, forecasting and anomaly detection models, and cost-versus-revenue analytics. Identify and implement AWS and GCP savings opportunities, automate governance with Python, Terraform, and Lambda, and partner with Engineering, Finance, Product, and leadership on cloud costs, forecasting, reporting, and architecture decisions.

Designs and maintains scalable data pipelines and ETL/ELT workflows using Python, SQL, and Azure Databricks. Manages large structured and semi-structured datasets, data models, monitoring, quality controls, troubleshooting, and production support. Integrates LLM and Generative AI capabilities, including APIs, embeddings, prompt engineering, and RAG workflows, into data solutions. Collaborates with engineering, analytics, and business teams while following security, CI/CD, documentation, and maintainability best practices.

Design, build, and optimize GCP data ecosystems, including pipelines, data lakes, warehouses, storage architectures, and processing infrastructure. Gather requirements, model data, troubleshoot complex issues, tune performance, monitor systems, and support new functionality. Collaborate directly with clients and cross-functional teams, while mentoring junior data engineers and leading technical improvement discussions.

Provides technical leadership for Azure data engineering projects, including solution architecture, data modeling, pipelines, data integration, code quality, QA, performance optimization, governance, security, and documentation. Collaborates with stakeholders and project managers to define scope, manage risks, and deliver scalable, efficient solutions. Requires strong Python, SQL, PySpark, Databricks, Azure Data Factory, Delta Lake, Synapse, Spark, data warehousing, governance, and Azure DevOps expertise.

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.

Builds and maintains scalable data pipelines, data architecture, ETL infrastructure, and analytics tools. The role assembles complex datasets, improves data delivery and infrastructure scalability, supports cross-functional stakeholders, and enables actionable business insights. Required expertise includes SAS, Python, Java, MATLAB, SQL and NoSQL databases, Hadoop-based data warehousing, MapReduce, Hive, and Presto. The position requires 5–8 years of related experience and a bachelor’s degree or equivalent experience.

Develop AI-powered product features for local discovery, including personalized recommendations, content curation, search, and ranking. Build data pipelines by scraping, cleaning, and structuring datasets for machine learning models. Collaborate with product, design, and engineering teams to integrate AI into the platform, while continuously improving machine learning capabilities and user experiences.

Build and maintain scalable data platforms, including Airflow ingestion pipelines, Kafka streaming, Spark workloads, data lakes, and database solutions. Develop secure, containerized applications for air-gapped environments; optimize ingestion, queries, and production performance. Ensure data quality, lineage, observability, and SLA compliance while translating customer needs into data products. Create documentation and operational runbooks, troubleshoot production issues, and collaborate with engineers, analysts, and business stakeholders.

Leads the design, development, and maintenance of scalable ETL/ELT pipelines and data products across distributed, multi-cloud environments. Defines data engineering architectures, resolves complex production issues, and designs efficient data solutions. Provides technical leadership and mentorship while collaborating with architects, engineers, and business stakeholders. Requires expertise in big data, AWS services, data warehousing, databases, data modeling, orchestration, streaming, security, observability, version control, containers, and modern development practices.

Develop and maintain data pipelines, models, database structures, and orchestrations on Microsoft Azure. Transform vessel, project, campaign, and third-party data into high-quality datasets and reports. Collaborate with business stakeholders to refine requirements, support data scientists and report developers, and design modern cloud data solutions. The role requires strong data modeling, ER modeling, SQL or programming experience, and at least five years of relevant experience.

Designs and maintains scalable data engineering solutions using Python, SQL, and Snowflake. Builds ETL/ELT pipelines, data warehouses, data lakes, and React interfaces while integrating generative AI, LLMs, RAG, and intelligent automation. Responsibilities include Snowflake architecture, data governance, CI/CD, testing, performance optimization, stakeholder collaboration, Agile participation, business data analysis, and mentoring junior developers.

Designs and maintains scalable data engineering solutions using Python, SQL, and Snowflake. Builds ETL/ELT pipelines, data warehouses, data lakes, and React applications while integrating generative AI, LLMs, and RAG architectures. Implements Snowflake data architectures, CI/CD, testing, monitoring, and governance practices. Collaborates with stakeholders and cross-functional teams to deliver data-driven solutions, analyze business data, and provide technical leadership and mentorship.

Develop and maintain SAP ABAP solutions, customizations, reports, workflows, and integrations. Build SAP data extraction and integration pipelines using ODP, CDS Views, OData, and SAP Datasphere. Translate business requirements, especially SAP SD processes, into scalable technical solutions and integrate SAP data with cloud platforms. Collaborate with functional consultants, data engineers, stakeholders, and external partners while independently resolving technical issues and strengthening internal SAP capabilities.

Design, build, and maintain centralized data pipelines, warehouses, lakes and hubs. Assemble complex datasets, implement ETL processes, build analytics tools and reports, support cross-functional teams, ensure secure cross-region data separation, and create product data tools to enable insights on acquisition and operational metrics.

Design, build, and optimize scalable data pipelines and event-driven architectures on GCP. Develop Python applications, integrate external APIs, manage BigQuery and cloud storage solutions, and automate deployments with Cloud Build. Ensure pipeline performance, scalability, cost efficiency, data quality, governance, lineage, and security while collaborating with cross-functional teams and documenting technical solutions.

Design, build, and optimize scalable ETL/ELT pipelines and event-driven data architectures on GCP. Develop Python applications, integrate external APIs, deploy cloud services, automate CI/CD with Cloud Build, and manage data quality, governance, lineage, security, performance, and cost efficiency. Collaborate with cross-functional teams to deliver scalable data solutions and maintain technical documentation.

Own the architecture, technical direction, reliability, cost efficiency, and adoption of Atlassian’s analytical data platform. Build foundational ingestion, transformation, orchestration, compute, and consumption components; establish multi-tenant standards and SLOs; and lead cross-team technical initiatives. The role requires hands-on development, distributed systems expertise, batch and streaming architecture design, cloud experience, and mentorship of senior engineers.

Build Gather AI’s data foundation from scratch by creating PostgreSQL extraction pipelines, dbt models, semantic metrics, data quality controls, tenant isolation, lineage, and documentation. The role includes warehouse data movement, ingestion-boundary validation, CI/CD delivery, safe backfills, on-call ownership, and collaboration across product, ML, security, and integration teams. Engineers will initially support the Drone product and later expand the platform to additional robotics and warehouse products.

Build and deploy standalone responsive HTML, CSS, and JavaScript dashboards replacing Power BI for government water programs. Integrate REST/JSON APIs and Google Sheets CSV feeds, create configurable indicators and data schemas, ensure accessibility and cross-browser compatibility, support government deployment, document the system, conduct demos, and transfer knowledge to operations teams over a six-month consultancy.

Own end-to-end design and delivery of IoT and real-time data platform features: define architecture, build Node.js backends and React/Vue dashboards, integrate device telemetry (MQTT/WebSockets), manage MongoDB and time-series data, ensure CI/CD and cloud deployments, and mentor junior developers.

Leads cybersecurity data-source onboarding and integration for Zscaler’s security platform. Builds and automates Python/API-based data transformations, maps and normalizes security data, manages pipeline lifecycle activities, troubleshoots data quality, and identifies security gaps. Partners with cybersecurity SMEs and cross-functional teams to create implementation plans, communicate technical findings, and provide product improvement feedback. The role requires expertise in security platforms, diverse security-tool integrations, data modeling, SQL, and Python.

Build and maintain ETL pipelines, data transformations, data models, BI dashboards, and reports. Design scalable relational database schemas and use Python or Java, SQL, AWS, and workflow orchestration to solve complex production data problems. Translate technical findings into business insights while collaborating with technical and non-technical stakeholders. Apply Agile practices and support enterprise and SaaS release cycles, with healthcare compliance experience preferred.