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.

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.

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.

Designs, deploys, and operates production-scale data platforms in fully air-gapped environments. Builds Airflow and Spark pipelines for high-volume sensor, satellite, and third-party data; develops PostgreSQL, TimescaleDB, PostGIS, and DuckDB solutions; manages Docker-based deployments and MinIO storage; and handles data quality, observability, documentation, troubleshooting, and SLA compliance without cloud services.

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.

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.

Designs, builds, and maintains scalable ETL pipelines and data products across multi-cloud, multi-region environments. Investigates and resolves technical, procedural, and operational data issues while applying disciplined development practices. The role requires expertise in data engineering patterns, architecture, big data, programming, warehousing, orchestration, databases, data modeling, streaming, security, version control, containerization, monitoring, and logging.

Design, build, and optimize scalable data pipelines, ETL/ELT workflows, dbt transformation projects, SQL data models, and AWS cloud data solutions. Ensure data quality, governance, security, and pipeline reliability while supporting BI initiatives. Collaborate with stakeholders, lead technical discussions, review code, mentor junior engineers, and develop reusable documentation and best practices.

Designs and optimizes scalable data architectures, ingestion pipelines, ELT/ETL transformations, cloud data warehouses, and orchestration workflows. The role uses Python, SQL, dbt, Spark, and platforms such as Snowflake, Databricks, and BigQuery. Responsibilities include data quality validation, query and cost optimization, dimensional modeling, access controls, data masking, compliance, and enterprise data warehouse migrations. Familiarity with streaming technologies and mandatory certification in Snowflake, Databricks, or Google Cloud is required.

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 support scalable healthcare data platforms, unified data models, datasets, and ad hoc data access solutions. Extract and integrate heterogeneous healthcare data, improve data quality, support acquisitions, and model data for BI, software applications, machine learning, and AI. Collaborate with product, engineering, business, and clinical stakeholders while following software development lifecycle best practices.

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.

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.

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.

Designs and optimizes scalable data pipelines and architectures supporting analytics and operational needs. The role develops ETL, streaming, data quality, governance, and cloud-based workflows using modern big data technologies. Responsibilities include translating business requirements into technical solutions, monitoring data systems, improving reliability and performance, and providing technical guidance on data modeling, storage, and metadata management.

Designs and develops scalable Microsoft Fabric and Azure data platforms, including ETL/ELT pipelines, APIs, integrations, data warehouses, lakehouses, and analytical data models. The role supports Power BI reporting, Salesforce integration, data quality, monitoring, CI/CD, performance optimization, production troubleshooting, and collaboration with business, CRM, reporting, and engineering teams.

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.

Build and operate scalable financial market data pipelines for Binance stock products and AI applications. Responsibilities include sourcing, ingesting, modeling, processing, storing, and serving real-time and historical data; designing multi-market data frameworks; optimizing Flink pipelines; and establishing quality, reconciliation, monitoring, lineage, and recovery systems. The role also evaluates vendors and data sources, defines data contracts, and collaborates with trading, product, AI, legal, and compliance teams.

Build and operate scalable financial market data infrastructure, including source evaluation, ingestion, cleansing, modeling, real-time and batch processing, quality governance, reconciliation, monitoring, and data services. Develop Flink-based pipelines supporting trading, research, and AI applications. Partner with product, trading, compliance, procurement, and engineering teams to define data contracts, source strategies, and consistent financial data semantics across markets.

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, 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.