Top Data Engineer Jobs in Gurgaon

Build and maintain scalable data pipelines and products across structured, unstructured, and graph data. Design data models, business domains, enterprise ontologies, and semantic solutions while optimizing workload performance, cost, and scalability. Translate business concepts into effective data solutions and independently own technical problems from design through production using Snowflake, SQL, dbt, Python, Snowpark, and orchestration frameworks.

Designs, develops, and maintains data solutions, pipelines, and ETL processes using Snowflake. Ensures data quality, reliability, and performance across data systems; troubleshoots pipeline issues, documents workflows, collaborates with cross-functional teams, and supports data migration and deployment. The role also involves contributing to technical discussions, independently solving problems, and mentoring junior team members.

Build and deliver cloud-based data engineering projects using Python, PySpark, and AWS services including Glue, Lambda, S3, Redshift, and DynamoDB. Collaborate with global business partners, lead projects, apply software development and quality practices, and support data analysis and Tableau dashboards. Insurance and financial industry knowledge, agile delivery experience, data warehousing, and data modeling skills are valuable.

Design, build, and maintain scalable ETL and ELT pipelines, data transformations, and analytics-ready datasets. Integrate data from databases, APIs, files, SharePoint, and cloud storage. Implement quality checks, monitoring, logging, alerts, and production troubleshooting. Optimize processing performance, cost, and reliability while documenting data mappings and operational procedures. Collaborate with BI analysts, application teams, and stakeholders to deliver dependable data solutions.

Design, optimize, and productionize scalable ETL/ELT pipelines and agentic workflows using Databricks, PySpark, SQL, GCP, BigQuery, and Delta Lake. Implement data quality controls, monitoring, validation, exception handling, testing, deployment, troubleshooting, and reusable frameworks. Collaborate with business, data engineering, and platform teams to automate operational processes and support production-grade data solutions.

Leads enterprise data engineering initiatives, collaborating with technical and business stakeholders to define requirements, integrate data, and establish data management standards. Designs and implements cloud data pipelines, ETL processes, data warehouses, CDC, event streaming, and complex data transformations using AWS, S3, and Glue. Develops data models and catalogs, evaluates data quality, supports governance, and guides adoption of Parquet, Iceberg, and unstructured data technologies. Presents technical solutions to senior decision-makers and promotes effective design practices.

Build, maintain, test, and optimize scalable data architectures and platforms. Extract, transform, and load data for analysts and scientists; source and integrate new data; perform root cause analysis; and develop cost-effective cloud-native solutions. Collaborate with technology and architecture teams, support DevOps adoption, understand data structures and metrics, and apply responsible AI engineering through production delivery.

Designs and supports scalable data pipelines, ETL/ELT solutions, and lakehouse data products using Azure Databricks, PySpark, SQL, and Snowflake. Migrates legacy workloads, builds data quality and monitoring frameworks, optimizes performance and costs, and supports governance, security, CI/CD, testing, production operations, and root-cause analysis. Partners with stakeholders to deliver healthcare data products and contributes to AI-powered application development.

Build, test, optimize, and support batch and real-time data pipelines using Spark, Databricks, Kafka, Scala, Python, and SQL. Troubleshoot performance and production issues, analyze execution plans and logs, develop Iceberg or Delta Lake solutions, and follow architecture, security, governance, testing, and operational standards. Collaborate with technical teams, resolve application issues, conduct code reviews and validation, support deployments, and provide guidance to junior analysts.

Develop and maintain batch and streaming data pipelines on Google Cloud. Build complex data transformations, orchestrate ETL workflows, perform data migrations, and maintain data warehouses and data lakes. The role requires expertise in BigQuery, Composer, GCS, Cloud SQL, Dataflow, Dataproc, IAM, Python, SQL, and PySpark, along with strong database, data modeling, communication, and problem-solving skills.

Executes specialized data engineering projects and activities independently, manages short-term team initiatives, improves processes, and may supervise less-experienced professionals. The role requires broad discipline knowledge, operational decision-making within a defined scope, ownership of professional deliverables, and a focus on improving day-to-day activities.

Designs, develops, optimizes, and maintains scalable data pipelines and workflows using Azure and Databricks. Responsibilities include ETL/ELT development, data modeling, validation, monitoring, troubleshooting, performance tuning, regulatory compliance, and data quality assurance. The role collaborates with business teams, analysts, architects, and developers in an Agile environment to support enterprise reporting, analytics, and insurance-related data use cases.

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.

Build and maintain semantic models, data marts, certified reports, dashboards, and self-service analytics. Partner with business and data teams to define KPIs, reporting requirements, and standards. Analyze data, improve report performance and usability, answer questions about metrics and business processes, and support enterprise analytics strategy using systems such as Oracle, Salesforce, and Workday.

Designs, builds, and manages scalable Azure Databricks data pipelines and Lakehouse workflows using Spark SQL and PySpark. Maintains Enterprise Data Warehouse architecture, data models, schemas, and data quality processes. Automates ingestion, testing, deployment, and monitoring using CI/CD tools. Collaborates with analysts, data scientists, and business stakeholders to support analytics initiatives, while ensuring data governance, privacy, security, and compliance across cloud, on-premises, and hybrid environments.

Design, build, and maintain scalable ETL pipelines, data warehouses, data lakes, and analytics platforms. Develop Streamlit dashboards, integrate PostHog behavioral data, create LangChain workflows, and use deepeval for model validation. Optimize SQL and database schemas, ensure data quality, governance, security, and privacy, monitor pipeline health, troubleshoot failures, and collaborate with data scientists and cross-functional teams.

Maintain Python-based web scraping configurations, monitor errors and data blockages, coordinate scraping requirements with stakeholders, and report on scraping activities. Build and optimize pipelines for data lake ingestion and transformation using ETL, data modeling, cloud, database, and infrastructure tools. Collaborate with data scientists and analysts while ensuring data security, compliance, testing, and maintainability.

Designs and maintains scalable GCP data infrastructure, including warehouses, data models, pipelines, and distributed processing workflows. Uses Dataflow, Apache Beam, BigQuery, Dataproc, Cloud Composer, Pub/Sub, and Cloud Storage for batch and real-time processing. Manages Git-based CI/CD, data quality, cloud migrations, workflow orchestration, and data lifecycle policies.

Designs and operates production data pipelines, ingestion and scraping systems, distributed processing architectures, APIs, caching services, and data-quality frameworks. The role uses Python, Spark, cloud data platforms, workflow orchestration, SQL, NoSQL systems, Kubernetes, and observability tools. It includes production ownership, incident response, technical documentation, code reviews, mentoring, and collaboration with data science and platform teams to support machine-learning and forecasting systems.

Design, build, and optimize scalable GCP data pipelines for massive eCommerce clickstream datasets. Develop Airflow workflows, use BigQuery for storage and transformation, integrate Google Analytics exports, and implement monitoring, validation, automation, and data quality controls. Collaborate with data science, product, and business teams while documenting best practices and optimizing performance, cost, and scalability across datasets exceeding 100 billion rows.

Build repeatable ETL processes that import, transform, cleanse, and validate data from CSV files and APIs into SQL Server. Develop resilient C#/.NET services, Azure Functions, and console utilities integrating with platforms such as Salesforce. Implement asynchronous processing, authentication, retry logic, Entity Framework Core, LINQ, testing, and CI/CD pipelines. Develop React interfaces, maintain reporting datasets and KPI queries, and document data flows and quality issues.

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 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.

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.