Top Data Engineer Jobs in Gurgaon

Build and optimize trusted data models, Power BI semantic models, dashboards, reports, and analytical datasets supporting workplace and employee experience insights. Partner with pipeline engineers, product managers, analysts, and stakeholders on ingestion, transformation, data quality, governance, documentation, access controls, monitoring, and reporting reliability. Translate business questions into maintainable data solutions using SQL, Power BI, Azure services, Microsoft Fabric, and scripting tools.

Design, develop and optimize ETL pipelines and SQL code using SSIS, T-SQL, Teradata and Denodo. Perform data profiling, source-target mapping, SQL tuning, testing and implementation. Support rollouts, fixes and enhancements, and improve data-loading programs and services. Communicate effectively with stakeholders and end users.

Designs, develops, and maintains data solutions, pipelines, and scalable ETL processes using Snowflake and cloud technologies. Ensures data quality, supports data migration across systems, troubleshoots complex data issues, and improves workflow performance. The role acts as a subject matter expert, makes team decisions, coordinates cross-functional initiatives, and mentors junior engineers.

Design, develop, and maintain data solutions, pipelines, and ETL processes using AWS Neptune and graph technologies. Ensure data quality, optimize workflows, model graph data and ontologies, and support Knowledge Graph and Graph RAG use cases. Collaborate with cross-functional teams, document processes, contribute to technical discussions, and mentor junior team members while working independently as a subject matter expert.

Build and maintain scalable AWS data platforms, pipelines, storage, orchestration, and infrastructure using Terraform, Snowflake, Airflow, dbt, and ingestion tools. Develop SQL and Python transformations, containerized services, data quality checks, monitoring, lineage, governance, and cost optimization. Support streaming and event-driven systems, troubleshoot platform issues, document standards, and collaborate with senior engineers and cross-functional data teams. Exposure to MLOps, CI/CD, GitHub Actions, and regulated financial services is beneficial.

Designs, develops, and maintains AWS-based data models, data lakes, warehouses, and ETL/ELT pipelines. Builds integrations, transformations, ingestion processes, and medallion architecture using SQL, Python, and PySpark. Supports legacy modernization, data security, governance, performance optimization, monitoring, and troubleshooting. Collaborates with technical and business teams to deliver reliable analytics and reporting solutions while documenting data architecture and flows.

Designs and implements data pipelines, optimizes data processing and storage, ensures data accuracy and performance, provides technical support, and collaborates with data scientists, analysts, and stakeholders to resolve data issues and meet data requirements.

Execute large-scale data platform migrations to Snowflake, adapting approximately 1,000 datasets and pipelines. Build, test, monitor, and support reliable Apache Airflow workflows, including Airflow 2-to-3 migration. Work with Spark, PySpark, Kafka, Iceberg, Parquet, and AWS S3 while ensuring data quality, reliability, performance, and cost optimization. Collaborate with data platform teams on modernization initiatives and time-sensitive migrations through March 2027.

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.

Design and implement data pipelines, optimize processing and storage, ensure data accuracy and performance, provide technical support, and collaborate with data scientists, analysts, and stakeholders to resolve data issues and meet data requirements.

The job description does not provide information about responsibilities, qualifications, technologies, compensation, or other employment requirements.

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 and builds end-to-end data platforms on Google Cloud for regulated-sector clients. Responsibilities include developing reliable pipelines, modeling scalable data systems, optimizing performance and cost, ensuring data governance and regional compliance, and applying cloud networking and security practices. The role requires strong SQL and Python skills, client-facing communication, and ownership of project outcomes.

Build and operate Microsoft Fabric data pipelines for six heterogeneous sources. Responsibilities include ingestion, mirroring, CDC, incremental loads, Delta Lake raw-layer management, PySpark standardization, entity-resolution support, data quality checks, monitoring, troubleshooting, documentation, lineage, and Spark/Delta performance optimization. The role owns production ingestion pipelines, source-to-target mappings, exception handling, and operational run documentation.

Designs data models, reverse-engineers physical data models, addresses data integration challenges, and supports data architecture strategy. The role requires advanced SQL, ETL, Snowflake, CI/CD, Python, and AWS services including S3 and Glue, with emphasis on developing scalable long-term data solutions.

Build and maintain organizational data pipelines using Python, SQL, Airflow, and Snowflake. Cleanse, normalize, and enhance data; develop ingestion workflows for APIs, files, and databases; optimize transformations and datasets; and ensure pipeline quality, reliability, and performance. Collaborate with engineering, analytics, and product teams in an Agile environment while using integration tools and data engineering best practices across development, testing, and operations.

Maintain and enhance an Azure data warehouse, developing ETL processes, data mappings, transformations, and API-based integrations. Ensure data integrity, quality, privacy, security, performance, and scalability while following software engineering practices. Serve as a business data subject matter expert and collaborate with Applications, Finance, Data Governance, and IT teams. The role requires strong Databricks, Azure Synapse, SQL, and analytical skills, with Power BI and Azure DevOps experience desirable.

Build and support batch and streaming data ingestion pipelines in a cloud-native environment. Develop monitoring dashboards, alerts, data warehouses, data marts, aggregations, metrics, and KPIs. Collaborate with product teams to create complex datasets, data dictionaries, catalogs, documentation, and actionable product insights. Use modern data engineering technologies including Spark, Python, SQL, Airflow, cloud platforms, Docker, Kubernetes, and Terraform.

Support, maintain, troubleshoot, and optimize production Databricks applications, Spark jobs, SQL queries, Delta Lake pipelines, clusters, workflows, and workspace configurations. Implement data quality checks, logging, alerting, governance, security, and cost-management practices. Collaborate on enhancements and bug fixes, reduce technical debt, document operational processes, and participate in EST-aligned support rotations.

Design and implement data pipelines, optimize data processing and storage, ensure data accuracy and performance, provide technical support, and collaborate with data scientists, analysts, and stakeholders to resolve data issues and meet requirements.

Support business analysis by gathering and documenting requirements, analyzing business processes, performing basic data analysis, and collaborating with senior analysts to learn best practices.

Build and maintain FinOps data pipelines, BigQuery and dbt models, cost attribution systems, forecasting and anomaly detection models, and cost-versus-revenue analytics. Identify and implement AWS and GCP savings opportunities, automate governance with Python, Terraform, and Lambda, and partner with Engineering, Finance, Product, and leadership on cloud costs, forecasting, reporting, and architecture decisions.

Designs and maintains scalable data pipelines and ETL/ELT workflows using Python, SQL, and Azure Databricks. Manages large structured and semi-structured datasets, data models, monitoring, quality controls, troubleshooting, and production support. Integrates LLM and Generative AI capabilities, including APIs, embeddings, prompt engineering, and RAG workflows, into data solutions. Collaborates with engineering, analytics, and business teams while following security, CI/CD, documentation, and maintainability best practices.

Design, build, and optimize GCP data ecosystems, including pipelines, data lakes, warehouses, storage architectures, and processing infrastructure. Gather requirements, model data, troubleshoot complex issues, tune performance, monitor systems, and support new functionality. Collaborate directly with clients and cross-functional teams, while mentoring junior data engineers and leading technical improvement discussions.

Provides technical leadership for Azure data engineering projects, including solution architecture, data modeling, pipelines, data integration, code quality, QA, performance optimization, governance, security, and documentation. Collaborates with stakeholders and project managers to define scope, manage risks, and deliver scalable, efficient solutions. Requires strong Python, SQL, PySpark, Databricks, Azure Data Factory, Delta Lake, Synapse, Spark, data warehousing, governance, and Azure DevOps expertise.