Top Data Engineer Jobs in Gurgaon

Develop and configure ZAIDYN data pipelines and analytics-ready and AI-ready data products. Responsibilities include ETL development, data modeling, data quality rules, KPI computation, cloud and big-data integration, testing, deployment, documentation, and client delivery. The role translates business requirements into technical solutions, collaborates with global engineering and analytics teams, supports project planning, mentors junior colleagues, and manages assigned workstreams.

Build, maintain, and optimize scalable batch and real-time data pipelines using PySpark, Spark, and cloud platforms. Develop ETL/ELT processes, data ingestion strategies, data lakes, and Big Data frameworks. Support data platform architecture, governance, security, performance tuning, and deployment across AWS, Azure, or GCP. Collaborate with data scientists, analysts, architects, and engineers to ensure reliable, secure, and accessible data.

Design, build, and maintain scalable cloud-based ETL/ELT data pipelines (Python, PySpark, SQL, Snowflake) supporting P&C insurance pricing, rating, underwriting, and actuarial analytics. Collaborate with actuaries, data scientists, analysts, and cloud engineers to deliver reusable data frameworks, batch/incremental ingestion, data quality, and transformations for GLM and predictive models.

Lead design and implementation of scalable Azure/Databricks data architectures. Build and optimize PySpark/SQL ETL/ELT pipelines, enforce data quality using Great Expectations/DLT, manage governance with Unity Catalog and Purview, tune Spark clusters, orchestrate pipelines with ADF/Synapse, and support analytics and ML workloads in an Agile environment.

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.

Build and maintain scalable batch and real-time data pipelines, ETL/ELT processes, data lakes, and big-data processing frameworks. Use PySpark, Spark, Python, SQL, cloud platforms, and orchestration tools to ingest, transform, secure, and optimize data. Collaborate with architects, data scientists, analysts, and engineers to ensure data quality, availability, governance, and performance across high-volume workloads.

Design, develop, and maintain data solutions and scalable ETL pipelines using Databricks. Ensure data quality, governance, and reliable data migration across systems. Lead and mentor a team, make technical decisions, optimize workflows, and troubleshoot data issues to support cross-team data accessibility.

Develop and maintain data pipelines, ETL processes, and data platform solutions using Databricks. Collaborate with integration and data architects to align platform components with business requirements, troubleshoot technical issues, optimize processing performance, and support data quality and workflow efficiency. The role also involves contributing to platform design, participating in technical discussions, and mentoring junior team members.

Design, develop, and maintain data solutions, pipelines, and ETL processes for data collection, integration, migration, and deployment. Ensure data quality, monitor and optimize pipeline performance, and collaborate with cross-functional teams to meet data requirements. The role requires independent execution, subject matter expertise, and proficiency in AWS Analytics, data warehousing, data modeling, database design, and cloud-based storage.

Design, build and maintain scalable data solutions and ETL pipelines, ensure data quality, migrate data across systems, optimize workflows, troubleshoot issues, and lead/mentor a team while collaborating with cross-functional stakeholders.

Build, maintain, and optimize scalable batch and real-time data pipelines using PySpark, Spark, Python, SQL, and ETL/ELT tools. Design cloud-based data platforms, data lakes, ingestion strategies, and Big Data processing frameworks across AWS, Azure, or GCP. Ensure data quality, security, governance, performance, and availability while collaborating with architects, analysts, data scientists, and engineers.

Design, develop, and maintain data solutions, pipelines, data models, and ETL processes. Ensure data quality, reliability, and efficient data migration across systems. Collaborate with multiple teams, provide technical solutions, make team decisions, and serve as a subject matter expert. Monitor and troubleshoot pipeline performance while supporting analytical and operational data needs.

Build scalable Snowflake data pipelines and ETL/ELT processes across projects. Ensure data quality, security, performance, and support structured and semi-structured data for analytics. Collaborate with architects, engineers, business teams, and Team Leads to translate requirements into solutions. Contribute to CoE guidelines, frameworks, reference architectures, proof-of-concepts, and GenAI initiatives using Snowflake Cortex capabilities.

Design, develop, and maintain data solutions, pipelines, and ETL processes across systems. Ensure data quality, monitor and optimize pipeline performance, gather data requirements, and collaborate with stakeholders. The role requires independent execution, team participation, and contribution as a data engineering subject matter expert.

Build scalable Snowflake data pipelines and ETL/ELT processes for structured and semi-structured data. Ensure data quality, security, and performance through advanced SQL, Snowpipe, Streams, Tasks, and stored procedures. Collaborate with architects, engineers, and business teams to translate requirements into solutions, support analytics and reporting, contribute to CoE guidelines and frameworks, and participate in proof-of-concept initiatives.

Design, develop, and maintain data solutions and ETL pipelines using Snowflake and related tools. Ensure data quality, lead cross-functional data strategy, mentor junior engineers, resolve complex data issues, and manage team decisions for large-scale data migrations and deployments.

Build scalable, secure Snowflake data pipelines and ETL/ELT processes across projects. Develop solutions using structured and semi-structured data, optimize Snowflake performance, write advanced SQL, ensure data quality and security, and collaborate with architects, engineers, and business teams. The role also contributes to CoE guidelines, frameworks, reference architectures, proof-of-concepts, and delivery-team support while tracking emerging Snowflake and GenAI capabilities.

Design and lead scalable Snowflake data architectures across ingestion, storage, transformation, and access. Establish data modeling standards, security practices, performance tuning, and architecture guidelines. Mentor teams, lead proofs of concept, contribute to centers of excellence, and support presales through proposals, estimations, solutioning, and client presentations. Collaborate with integration and data architects while evaluating Snowflake and generative AI capabilities.

Build and maintain data models, warehouses, and ETL pipelines using SQL, relational databases, big data technologies, cloud data services, and programming languages such as Python, Java, or Scala. Apply .NET development, DevOps, data security, and privacy practices while collaborating on innovative digital solutions. The role may involve national and international travel, professional development, conferences, and potential relocation or international assignments.

Design, build, and maintain cloud-based data solutions and ETL pipelines. Ensure data quality, optimize workflows for performance and cost, troubleshoot pipeline issues, collaborate with stakeholders, and mentor junior team members while providing cross-team technical leadership and advisory support.

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.

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.