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Azure ETL Data Engineer

PeopleNTech LLC

  • Santa Clara, CA
  • 30+ days ago
  • $50 Per Hour

Highlights

Key Responsibilities Design, develop, and maintain scalable data pipelines using Azure Data Factory / Azure Data Pipelines (ADT). Work with cross-functional teams (Data Analysts, Architects, DevOps) to understand data needs and deliver robust solutions.

Numbers & Facts

LocationSanta Clara, CA

Description

Role: Azure ETL Data Engineer
Location: McLean VA 22102
Max Rate: $50/hr

Key Responsibilities
  • Design, develop, and maintain scalable data pipelines using Azure Data Factory / Azure Data Pipelines (ADT).
  • Build and optimize ETL workflows to extract, transform, and load data from multiple structured and unstructured sources.
  • Develop data integration services and backend components using .NET (C#).
  • Write and optimize complex SQL queries, stored procedures, and performance-tuned database solutions.
  • Implement data quality checks, validation frameworks, and monitoring dashboards.
  • Work with cross-functional teams (Data Analysts, Architects, DevOps) to understand data needs and deliver robust solutions.
  • Ensure data security, compliance, and governance across pipelines and storage layers.
  • Troubleshoot data pipeline issues and drive root-cause analysis and resolutions.
  • Contribute to architecture discussions and recommend improvements for performance, scalability, and cost efficiency.

Required Skills & Qualifications
  • 8+ years of professional experience as a Data Engineer or similar role.
  • Strong programming skills in .NET / C# for backend or data integration components.
  • Deep expertise in SQL including query optimization, indexing, stored procedures, and relational database concepts.
  • Proven experience building ETL pipelines and data workflows.
  • Hands-on experience with Azure Data Factory / Azure Data Pipelines (ADT) (heavy/advanced experience required).
  • Knowledge of Azure storage services such as Azure SQL DB, Synapse, Data Lake Storage (ADLS), Blob Storage.
  • Familiarity with CI/CD pipelines, Git, and deployment automation.
  • Understanding of data modeling concepts (star schema, snowflake, SCD, normalization).

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