Top Data Engineer Jobs in Gurgaon

Designs and optimizes enterprise data pipelines using Azure Data Factory, Azure Data Lake Storage, SQL, Python, and cloud data platforms. Owns pipeline reliability, troubleshooting, automation, CI/CD, data lineage, documentation, code standards, and technical audits. Supports analytics and media teams while leading modernization initiatives involving Databricks, dbt, and DevOps. Serves as a subject matter expert for pipeline architecture, orchestration, schema evolution, monitoring, governance, and high-availability data processing.

Build scalable data engineering solutions on GCP, including BigQuery warehouses, Dataproc Spark/Hadoop clusters, Airflow orchestration, Dataflow pipelines, and Pub/Sub integrations. Develop automated ETL/ELT pipelines with Python, SQL, Scala, or Cloud Data Fusion; design OLAP and OLTP data models; and use GCP infrastructure, CI/CD, monitoring, and logging tools. Collaborate with analysts and business stakeholders while solving complex data challenges and adapting to evolving cloud technologies.

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.

Design, build, and maintain ETL/ELT pipelines using Azure Data Factory, Snowflake, and DBT. Develop optimized SQL for extraction and transformation, integrate data from multiple sources, ensure data quality and governance, monitor and troubleshoot pipelines, and collaborate with analysts, architects, and DevOps in a cloud-native environment.

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.

Lead the design, development, and optimization of Azure and Snowflake ETL/ELT pipelines using Azure Data Factory, DBT, and SQL. Build data integration workflows, ensure pipeline reliability and data quality, enforce governance and documentation standards, and collaborate with stakeholders, analysts, architects, and DevOps teams. The role also supports cloud-native data environments, large-scale datasets, troubleshooting, and technical solution development.

Build and maintain scalable data pipelines, migrate legacy architectures to cloud-based AI-ready platforms, develop semantic layers, and support analytics, business intelligence, and AI/ML use cases. The role collaborates with stakeholders, data scientists, analysts, and product teams while ensuring data quality, security, governance, documentation, and workflow optimization.

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.

Lead data quality and governance initiatives: define frameworks, perform profiling/validation/cleansing, manage metadata/catalog/lineage, implement governance tools, monitor KPIs, coordinate remediation, and train stakeholders to ensure trusted, compliant enterprise data.

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 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.

Principal Data Engineer responsible for developing and supporting Databricks pipelines, integrating heterogeneous healthcare data sources, modeling unified data products, and ensuring data quality and availability. The role partners with business stakeholders, product teams, data engineers, and clinical leaders to gather requirements, create datasets, support ad hoc analytics, enable machine learning and AI use cases, and assist with acquisition integrations. Extensive healthcare data, cloud, SQL, PySpark, and Python experience is required.

Build and support fund accounting data solutions for asset management, including integrations, transformations, reconciliations, controls, and finance reporting products. Develop large-scale data pipelines using Databricks, Python, PySpark, and SQL; work with investment data, NAV calculations, accruals, corporate actions, fees, and general ledger information. Support cloud-based delivery, data quality, orchestration, CI/CD, and direct collaboration with US stakeholders.

Design, build, and optimize data pipelines and storage. Ensure data accuracy and performance, provide technical support, and collaborate with data scientists and stakeholders to resolve data issues.

Design and implement data models and ETL solutions. Reverse-engineer physical data models, analyze integration challenges, support data architecture and movement strategy, and ensure long-term, business-aligned data solutions.

As a Data Engineer, you will design, build, and maintain scalable data platforms and pipelines, optimizing workflows and ensuring security.

The Senior Azure Data Engineer will design, develop, and maintain data infrastructure and pipelines, ensuring data quality, integrity, and alignment with business objectives. Responsibilities include collaborating with teams and establishing best practices for data engineering.

Design, build, and maintain Azure-based data infrastructure and pipelines. Implement data models and schemas, ensure data quality, optimize distributed storage and processing (Databricks/PySpark), and establish engineering best practices while partnering with cross-functional teams.

Build and maintain data transformation pipelines, automated QA processes, and AI-assisted tools for debugging, documentation, and transformation. Develop prototype datasets, codify transformation logic, and support adoption of new technologies through mentoring, workshops, and training. Collaborate cross-functionally in an agile environment while applying Python, cloud services, SQL, CI/CD, machine learning, and data visualization tools.

Design, build, and maintain scalable data pipelines; migrate legacy architectures to cloud AI‑ready platforms; implement semantic layers; ensure data quality, security, and governance; collaborate with stakeholders and document architectures for analytics and BI.