Job Title: Data Engineer (Mid-Level to Senior)
Location: Hybrid / Onsite as needed (project-dependent)
Salary: $100,000 – $115,000
We are partnering with an organization seeking a skilled Data Engineer to support the design, build, and optimization of a modern data platform. This role focuses on developing scalable data pipelines and infrastructure that power analytics, reporting, and AI initiatives. The ideal candidate is comfortable working across Fabric and the modern Microsoft data stack (e.g., Azure Data Factory, Azure Data Lake Storage, Azure SQL Database / Managed Instance, Cosmos DB, etc.) and enjoys solving complex data transformation and integration challenges in a collaborative environment.
Your role:
- Design, build, and maintain ETL/ELT pipelines using Azure Data Factory and related tools.
- Architect and manage data lake and data warehouse solutions.
- Transform and move data across relational, NoSQL, and object storage systems.
- Collaborate with engineering and product teams to define data requirements and solutions.
- Monitor, troubleshoot, and optimize data pipelines for performance and reliability.
- Ensure data quality and integrity across systems.
- Document data flows, schemas, and architecture decisions.
- Contribute to data platform strategy and ongoing improvements.
What you’ve got:
- 3+ years of experience for mid-level candidates; 6+ years for senior-level candidates.
- Strong experience with Azure Data Factory or similar ETL/ELT tools.
- Solid understanding of database design (relational modeling, normalization/denormalization).
- Experience with data lakes and/or data warehouses (Azure Data Lake, Microsoft Fabric, etc.).
- Experience transforming structured, semi-structured, and unstructured data.
- Proficiency in SQL and experience with Azure SQL Database and/or Azure SQL Managed Instance.
- Familiarity with Azure data services and the Microsoft data ecosystem.
- Strong problem-solving skills and ability to work both independently and collaboratively.
Preferred:
- Experience with Cosmos DB or other NoSQL platforms.
- Knowledge of KQL and Azure Monitor / Data Explorer.
- Experience with object storage and file-based data architectures.
- Exposure to analytics or data science tools (Databricks, Azure ML, etc.).
- Understanding of data governance, lineage, and cataloging best practices.