Top Data Engineer Jobs in Delhi

Design and optimize scalable ETL/ELT pipelines using Databricks, PySpark, SQL, GCP, BigQuery, and Delta Lake. Build agentic workflows that automate data validation, troubleshooting, and operational processes. Develop reusable frameworks, implement data quality and monitoring controls, optimize workloads, and support testing, deployment, productionization, and ongoing enhancements across data engineering solutions.

Build and maintain scalable ETL/ELT pipelines, data ingestion workflows, backend services, APIs, and database systems. Clean, transform, validate, and model structured and unstructured data while implementing quality controls, monitoring, and production reliability. Develop Agentic AI and workflow automation integrations, deploy applications across cloud and on-premises environments, and collaborate with customers and cross-functional teams to deliver AI/ML solutions from proof of concept to production.

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.

Designs, builds, and maintains scalable data infrastructure, pipelines, ETL/ELT workflows, and cloud-based integration solutions. Responsibilities include transforming raw data, implementing data quality frameworks, optimizing queries and data models, monitoring pipelines, and improving data architecture for analytical and operational needs.

Designs and supports batch and incremental data ingestion pipelines, ETL workflows, SQL transformations, and Airflow orchestration. Investigates data quality issues, reconciles discrepancies, performs data lineage analysis, supports cloud migration initiatives, and validates data across systems. The role also provides production support, resolves incidents, develops reusable engineering components, and collaborates with stakeholders on data requirements.

Designs and supports batch and incremental data ingestion pipelines, ETL workflows, SQL transformations, BigQuery data platforms, and Airflow orchestration. Investigates data quality issues, validates data across systems, performs lineage and source-to-target analysis, supports cloud migration initiatives, and resolves production incidents. Collaborates with stakeholders and engineering teams to define data requirements and develop reusable data engineering components.

Design and support batch and incremental data ingestion pipelines, ETL workflows, SQL transformations, and Airflow orchestration. Troubleshoot data quality, reconciliation, lineage, discrepancies, and production incidents. Support cloud data migrations, validate data across systems, collaborate with stakeholders, and develop reusable engineering components using BigQuery, SQL, Python, Airflow, and GCP services.

Design, develop, and support batch and incremental data ingestion pipelines, ETL workflows, Airflow orchestration, and cloud data platforms. Investigate data quality issues, validate data across systems, perform lineage and source-to-target analysis, support data migrations, resolve production incidents, and build reusable engineering components. Collaborate with stakeholders and technical teams on data requirements and process improvements.

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.

Develop and automate end-to-end data pipelines and integration solutions using Oracle Data Integrator, MuleSoft, GoldenGate, SQL, and scripting. Define implementation requirements, influence data architecture and standards, provide technical expertise, support analytics, and lead process improvements across global teams.

Designs and maintains scalable AWS, Snowflake, and dbt data pipelines and transformation frameworks. Builds ETL/ELT workflows, dimensional models, data quality frameworks, and optimized SQL workloads for structured and semi-structured data. Responsibilities include pipeline orchestration, monitoring, troubleshooting, CI/CD, legacy ETL modernization, documentation, production support, and collaboration with technical teams. The role also contributes to architecture discussions, Agile delivery, code reviews, and mentoring junior engineers.

Build and maintain governed finance data products on Microsoft Fabric and Databricks. Responsibilities include developing PySpark and Python ingestion pipelines, implementing incremental loading and CDC, optimizing Delta Lake tables, managing Azure Data Factory pipelines and Analysis Services cubes, designing Power BI semantic models, and ensuring version control, testing, monitoring, governance, and data contract compliance. The role also involves stakeholder collaboration, mentoring junior engineers, and applying enterprise data architecture standards.

Build and support large-scale, real-time kdb+ analytics platforms processing billions of daily messages. Develop kdb Insights or KDB-X modules, packages, pipelines, performant q queries, and functions for trading desks. Create SRE tools to detect performance bottlenecks across q, Python, and Java processes running continuously in AWS. Support ingestion stability, Kubernetes deployments, PyKX data processing, and cloud-native analytics infrastructure.

Build and operate production data platforms, tooling, and infrastructure supporting autonomous pipeline delivery. Own monitoring, alerting, DataOps, Kafka, Kubernetes, Terraform, AWS, Databricks/Spark, dbt, and Airflow environments. Improve platform reliability, troubleshoot production issues, and partner with product, analytics, and infrastructure teams. Champion AI adoption and automation while enabling high-throughput, low-latency data processing and strong developer experience.

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.

The description indicates a payroll-focused analyst role within the Payroll team, but provides no specific responsibilities, qualifications, technologies, salary, travel, or clearance requirements.

Designs, develops, tests, and maintains scalable ETL pipelines using Azure services and streaming technologies. Collaborates with architects, analysts, scientists, and business stakeholders to ensure data accuracy and integrity. Builds data models and documentation, optimizes workflows, monitors production data flows, troubleshoots issues, implements data-quality controls, and integrates cloud and on-premise storage while supporting governance, security, and privacy standards.

Own EULER’s data lifecycle, including instrumentation, validation, reconciliation, privacy, anonymization, canonical data modeling, analytics datasets, documentation, and production pipelines. The role requires strong SQL, software engineering practices, independent ownership, skepticism toward data quality, and clear communication. Preferred experience includes GDPR/LGPD, multi-tenant B2B SaaS, dbt, statistics, Cloudflare Workers, Postgres, Parquet, DuckDB, and Bubble.

Build and maintain data pipelines using Databricks, Python, SQL, and Spark. Apply data modeling, cloud data services, version control, and CI/CD practices. Support streaming pipelines, workflow orchestration, data governance, security controls, and downstream BI requirements. Experience with AWS or Azure is required, while Databricks certification, streaming technologies, orchestration tools, and Power BI exposure are preferred.

Develop and analyze Guidewire insurance data across policy, claims, and billing domains. Build complex SQL queries, perform data extraction and transformation, validate source-to-target mappings, investigate data quality issues, and support reconciliation, reporting, testing, and downstream analytics. Collaborate with business, Guidewire, and technical data teams while working with Snowflake and large-scale datasets.

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.

Develop and support scalable data operations, ETL pipelines, SQL integrations, Azure services, Power Platform automations, and Power BI reporting. Responsibilities include Azure Data Factory pipeline development, Logic Apps and Power Automate workflows, Power BI modeling and publishing, Azure resource administration, governance, monitoring, troubleshooting, security controls, documentation, and performance optimization.

Designs, codes, tests, and manages scalable data acquisition, integration, transformation, and management pipelines using Azure, Databricks, SAP Data Services, and DevOps practices. Builds data structures supporting Data as a Service, develops cloud and edge infrastructure, manages CI/CD pipelines and containerization, and collaborates with analysts, architects, product owners, and technical teams to deliver data-driven solutions.

Lead end-to-end data platform initiatives, designing self-service ingestion and transformation capabilities with governance, security, observability, cost controls, and CI/CD. Build and operate reliable batch and streaming pipelines, manage data correctness and migrations, establish SLOs and quality monitoring, and prepare governed data for AI agents. The role involves reviewing designs and code, documenting golden paths, mentoring through technical feedback, and collaborating across distributed engineering teams.