Job Description
Job Title: Azure Data Engineer
Location: Bellevue, WA
Duration: / Term: C2C
Experience Desired: 8+ Years
Job Description:
- This role builds, and maintains scalable data pipelines and lakehouse infrastructure on Microsoft Azure to support efficient extraction, transformation, and loading of data across batch and real-time workloads. It involves implementing and managing the Medallion Architecture (Bronze Silver Gold) using Azure Data Factory, Databricks-PySpark, and Azure SQL Database and Databricks unity catalogue.
- The role requires ensuring SLA-adherent data quality standards. Success is measured by pipeline reliability, data freshness SLA compliance, and the quality of Gold-layer datasets powering Power BI executive dashboards.
- The work supports organizational decision-making by delivering trusted, well-governed data to business executives and analytics consumers.
Required Skills:
- Experience building and optimizing big data pipelines using Azure Data Factory, PySpark, and SQL across structured and semi-structured data sets
- Hands-on experience implementing Medallion Architecture (Bronze/Silver/Gold)
- Experience with Delta Lake ACID transactions, incremental loading, schema evolution, partitioning strategies
- Experience performing root cause analysis on pipeline failures and data quality issues to resolve SLA breaches and identify platform improvement opportunities
Azure Foundational Services :
- Working knowledge of: Azure Data Factory (ADF), ADLS Gen2, Azure SQL Database, Azure Blob Storage, Azure Key Vault, Azure Monitor / Log Analytics, Azure Event Hubs, Microsoft Fabric Lakehouse, Azure Active Directory / Entra ID (RBAC, Service Principals)
Programming Languages:
- Proficiency in Python and PySpark for data transformation, pipeline automation, and large-scale distributed processing; strong SQL skills including window functions, CTEs, and query optimization across relational and lakehouse engines
Data Architecture:
- Solid understanding of Medallion Architecture, dimensional modeling (Star Schema, SCD Types 1/2/3), and the trade-offs between lakehouse, data warehouse, and data lake patterns
Pipeline Engineering:
- Ability to build robust ADF pipelines with ForEach, Lookup, Copy Activity, and Data Flows; incremental loading via watermark or CDC; error handling, retry logic, and dead-letter patterns
Data Quality Experience:
- Experience implementing SLA-based data quality checks (freshness, completeness, row count), monitoring via Azure Monitor and ADF diagnostic logs, and defining data quality agreements with business stakeholders.
DevOps for Data:
- Experience with Git-based workflows, ADF Git integration, CI/CD pipeline promotion across Dev/Test/Prod using Azure DevOps or GitHub Actions
Reporting Layer Awareness:
- Understanding of how Gold-layer data feeds Power BI DirectQuery vs. Import mode trade-offs, dataset refresh patterns, and semantic model collaboration with BI teams
- Ability to manage work across multiple concurrent pipeline projects, prioritize by business impact, and communicate status clearly to technical and non-technical stakeholders
Good to have skills:
- Experience with Microsoft Fabric (Lakehouse, Notebooks, OneLake, Fabric Pipelines) active migration or greenfield project
- Experience with real-time / streaming workloads using Azure Event Hubs or Structured Streaming in PySpark
- Experience delivering data platforms for executive-level reporting via Power BI semantic models
Key Skills:
Azure, Databricks, Data Factory, Power BI, Delta lake.
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