Develop scalable, well‑documented ETL/ELT pipelines using T‑SQL, Python, Azure Data Factory/Fabric Data Pipelines, and Databricks; implement best‑practice patterns for performance, security, and cost control.
Design relational and lakehouse models; create Fabric OneLake shortcuts, medallion‑style layers, and dimensional/semantic models for Power BI.
Build automated data‑quality checks, lineage, and observability metrics; contribute to CI/CD workflows in Azure DevOps or GitHub.
Gather requirements, demo iterative deliverables, document technical designs, and translate complex concepts to non‑technical audiences.
Research new capabilities, share findings in internal communities of practice, and contribute to reusable accelerators.
Collaborate with clients and internal stakeholders to design and implement scalable data engineering solutions.

Education – Bachelor’s in Computer Science, Information Systems, Engineering, or related field (or equivalent experience)
Experience – 2–3 years delivering production data solutions, preferably in a consulting or client ‑ facing role.
Technical Skills: Strong T ‑ SQL for data transformation and performance tuning.
Python for data wrangling, orchestration, or notebook ‑ based development.
Hands ‑ on ETL/ELT with at least one Microsoft service (ADF, Synapse Pipelines, Fabric Data Pipelines).
Project experience with Microsoft Fabric (OneLake, Lakehouses, Data Pipelines, Notebooks, Warehouse, Power BI DirectLake) preferred
Familiarity with Databricks, Delta Lake, or comparable lakehouse technologies preferred
Exposure to DevOps (YAML pipelines, Terraform/Bicep) and test automation frameworks preferred
Experience integrating SaaS/ERP sources (e.g., Dynamics 365, Workday, Costpoint) preferred

Data Engineer

Key skills