5–8 years of progressive experience in data engineering, data platform, or data architecture roles, with clear time spent as an architect or technical lead.
Production experience designing and delivering solutions on a modern cloud data platform — Databricks or Snowflake (Databricks strongly preferred).
Demonstrated experience defining reference architectures, platform standards, and patterns adopted by multiple teams.
Strong, hands\-on proficiency in SQL and Python (PySpark a plus) — able to prototype, review code, and debug production issues.
Deep expertise in data modeling — Kimball dimensional modeling, Data Vault 2\.0, and medallion architecture — with a clear point of view on when each applies.
Hands\-on experience with ETL/ELT design patterns, streaming / real\-time architectures, and ingestion from heterogeneous sources (ERPs, SaaS APIs, databases, files, events).
Solid experience designing for data governance — access controls, PII handling, row/column\-level security, masking, tokenization, data quality, lineage, and catalogs.
Production experience with at least one major cloud provider (Azure, AWS, or GCP); Azure preferred.
Working knowledge of orchestration (Airflow, Azure Data Factory, Databricks Workflows, dbt) and CI/CD for data workloads.
Experience designing BI and serving layers on top of the platform for modern BI tools (Power BI, Tableau, Looker, or similar).
Strong stakeholder management, technical writing, and presentation skills — able to produce architecture decision records (ADRs), diagrams, and standards documents.
Bachelor’s degree in computer science, Engineering, or equivalent practical experience.