Top Data Engineer Jobs in Gurgaon

Design and build a greenfield, multi-layer data warehouse and semantic metrics layer; separate analytics from OLTP; ensure data quality, traceability, tenant isolation, and high SLAs; enable linking of structured records to drone imagery/video; deliver self-service data access, observability, and infrastructure automation to scale across products and tenants.

The Senior Data Engineer will design and maintain scalable data pipelines, manage data warehousing on Google BigQuery, and implement CI/CD practices for data workflows.

Own the architecture, scalability, governance, and technical direction of Amtech’s cloud data platform, including warehouses, tenant data stores, feature stores, and event pipelines. Lead multi-tenant data architecture, hybrid AI data routing, data security, observability, and reliability standards. Build data foundations for analytics, machine learning, and AI agents, including vector databases and RAG pipelines. Provide technical leadership through design reviews, mentoring, roadmap planning, and cross-functional architecture collaboration.

Build and maintain scalable ETL/ELT data pipelines, data warehouse infrastructure, and data-processing workflows. Use BigQuery, Databricks, Dataproc, Python, Informatica, and Airflow to support analytics and business intelligence. Implement CI/CD and automation with Dataform and GitHub, collaborate with data scientists, analysts, and stakeholders, and optionally develop Tableau dashboards and integrate data with Anaplan.

Migrate SQL Server data products and analytical workloads to a modern cloud platform. Redesign legacy SQL, ETL processes, and business logic; build scalable pipelines and analytical models; validate data quality; collaborate with stakeholders; and improve platform reliability, performance, cost efficiency, and AI-assisted data engineering automation.

Build and maintain Snowflake and SQL Server data pipelines for an operational data store, including ingestion, transformation, schema modeling, migrations, CI/CD, data quality testing, and legacy batch-to-API modernization. Collaborate with domain teams and architects to support customer, loan, payment, and interaction data. Ensure data accuracy, lineage, observability, and performance while applying generative AI tools to accelerate development and testing.

Develop high-quality operational engineering capabilities and solutions. Collaborate with business partners, create designs, and optimize data processing pipelines using various technologies.

Build and enhance enterprise data pipelines into Databricks Unity Catalog and Delta Lake. Improve remediation and entity-matching layers (including D&B DataBlocks), integrate APIs, and support LLM-assisted remediation and CMG redesign projects.

Design, build, and maintain Microsoft Fabric/OneLake lakehouse solutions across Bronze/Silver/Gold layers. Create reusable ingestion and CDC pipelines, implement transformations and data quality frameworks, optimize Spark/PySpark/Python/SQL workloads, support Power BI Direct Lake, enforce governance (Purview, RBAC, PII/PHI), and troubleshoot production issues.

Design, build, and maintain high-volume ETL/ELT pipelines across Hadoop and AWS using Spark/PySpark, Scala, Glue, EMR, Step Functions and orchestration tools. Optimize performance, enforce data governance, support incidents, collaborate with stakeholders, and mentor junior engineers.

Design, deploy, and maintain AWS and Databricks data infrastructure, data lakes, and scalable ETL pipelines. Build custom ingestion services and connectors using Airbyte, Go, and Python; optimize performance, reliability, monitoring, and data quality. Implement cloud data governance, security, access controls, encryption, and compliance practices while collaborating with platform engineers, analysts, and stakeholders.

Designs and operates end-to-end data products on Databricks, including ingestion, transformation, streaming pipelines, APIs, applications, and curated datasets. Partners with stakeholders to clarify needs, prototype solutions, and deliver secure, scalable production systems. Responsibilities include data governance, testing, CI/CD, monitoring, optimization, troubleshooting, technical design, code reviews, and mentoring. The role requires strong SQL, Python, PySpark, cloud, event-streaming, backend, and data-modeling expertise.

Designs, builds, and maintains ETL/ELT pipelines feeding Microsoft Fabric Lakehouse platforms. Responsibilities include data ingestion, bronze/silver/gold layer development, CDC, incremental loading, data quality, metadata governance, performance tuning, monitoring, automation, and low-level design documentation. The role collaborates with architects, data modelers, and business analysts to deliver reliable, reusable datasets and promote self-service analytics while applying security, resilience, compliance, and cost-optimization standards.

Build and maintain scalable batch and real-time data pipelines using Kafka, Maxwell, Spark and dbt; design medallion-based data models; implement cloud-native data platforms (S3, Trino, BigQuery); optimize performance, ensure data quality, observability, and SLA adherence; enable analytics through trusted datasets and Metabase dashboards.

Design, build, and optimize scalable Databricks-based ETL/ELT pipelines using PySpark, Spark SQL and Delta Lake. Manage clusters, integrate cloud storage and streaming sources, ensure data quality and observability, and collaborate with data scientists and stakeholders to support analytics and ML workloads.

Lead design, development, and optimization of complex data systems and pipelines; oversee data engineering projects; ensure data quality; align solutions with business goals; mentor junior engineers and provide strategic guidance.

Lead delivery of cloud-based data engineering projects on AWS and Snowflake. Design high-level solutions, guide development and testing, optimize performance, deploy to production, manage risks, and communicate with stakeholders while mentoring team members and ensuring agile, timely delivery.

Design, build, and maintain ETL/ELT pipelines and data integration workflows into Snowflake using Azure Data Factory and DBT. Write and optimize SQL for extraction and transformation, monitor pipeline performance, enforce data quality and governance, and collaborate with analysts, architects, and DevOps in cloud-native environments.

Lead development and optimization of complex data systems and pipelines; oversee data engineering projects; ensure data quality; mentor junior engineers; align data solutions with business goals and provide strategic guidance.

Lead delivery of cloud-based big data projects: design, implement, and deploy ETL/data pipelines on AWS and Snowflake. Mentor team, collaborate with stakeholders, manage risks, and ensure high-quality, timely project delivery.

Lead design, optimization, and delivery of complex data systems and pipelines; manage data engineering projects; ensure data quality; mentor junior engineers; align data solutions with business goals and provide strategic guidance.

Design, build, and optimize large-scale ETL pipelines and distributed data applications (batch and streaming) using Spark and Scala. Ensure data quality and performance, deploy and manage workloads in cloud/container environments, troubleshoot production issues, and collaborate with product, engineering, and analytics teams to deliver scalable AdTech data solutions.

Design, build, and operate scalable batch and real-time data pipelines and analytics platforms. Partner with enterprise customers to define requirements, own technical delivery from discovery to adoption, and present architectures to stakeholders. Drive data governance, observability, CI/CD, and platform optimization while mentoring junior engineers and collaborating with Product, Engineering, and Customer Success teams to enable analytics and AI/ML applications.

Senior technical leader responsible for designing, building, and scaling cloud-native data platforms and pipelines. Partners with stakeholders, mentors engineers, enforces engineering best practices, ensures data modeling, governance, and quality, debugs production issues, and contributes to architecture, design, and Agile delivery to support business outcomes.

Design, build, and maintain cloud-native ELT pipelines and data models for large-scale financial datasets using dbt, BigQuery, and Airflow. Ensure data quality, reconciliation, observability, and performance; support APIs, CI/CD, and disaster recovery; collaborate with product and engineering teams and apply domain knowledge in securities and market data.