Data Engineer

Data Engineer / Data Vault Architect

3 Months

Remote / Hybrid

Tech Stack: Data Vault, Databricks, dbt,

We are looking for a hands-on Data Engineer / Data Vault Specialist for a focused 3-month contract engagement. Your core objective will be to establish an agreed, repeatable, and scalable approach for building our Gold layer on top of our existing Data Vault (Silver layer), working jointly with our Data Platform and Analytics Engineering teams.

Key Responsibilities & Scope of Work

Discovery

• Analyze current ways of working across the Data Platform and Analytics Engineering teams, clarifying ownership and handoffs between Silver and Gold layers.
• Review the existing Data Vault model (hubs, links, satellites) alongside current dimensional models built on top of it.
• Map downstream consumption needs across self-service BI, semantic layer plans, and upcoming agentic analytics ambitions.

Assessment & Strategic Recommendations

• Modeling Patterns: Recommend standardized patterns for translating Data Vault into dimensional models (e.g., Business Vault, PIT and Bridge tables, conformed dimensions, virtual vs. materialized marts, SCD handling).
• Layer Interface: Define a clear interface between Silver and Gold layers, utilizing PIT and Bridge tables to support exploratory queries and simplify dimensional design.
• Automation & Tooling: Identify automation opportunities across discovery, model design, and build (e.g., metadata-driven generation, dbt scaffolding, testing, documentation). Recommend dbt packages and native Databricks features to streamline delivery.
• Platform Integration: Identify reusable platform components across CI/CD, testing, orchestration, and monitoring.
• Semantic & AI Alignment: Evaluate Unity Catalog metric views as a semantic layer, assessing compatibility with ThoughtSpot and AI/agentic consumption.
• Performance & Governance: Incorporate performance optimization, history requirements, and data retention/deletion rules into the design.

Hands-On Delivery & Knowledge Transfer

• Apply the recommended modeling pattern jointly with internal teams on one pilot business domain .
• Upskill the Analytics Engineering team on Data Vault fundamentals applied to our specific model through hands-on pairing and workshops.
• Lead final stakeholder workshops and readout sessions.

Required Qualifications

• Data Vault & Dimensional Expertise: Deep, hands-on experience translating Data Vault (Hubs, Links, Satellites, PITs, Bridges) into Kimball dimensional models and semantic layers.
• Databricks Ecosystem: Strong proficiency with Databricks, Delta Lake, and Unity Catalog.
• dbt Mastery: Advanced experience using dbt Core/Cloud, dbt packages, scaffolding, and CI/CD testing patterns.
• Modern Analytics Tooling: Experience designing data models for self-service BI platforms (e.g., ThoughtSpot) and AI-driven analytics.
• Stakeholder & Mentoring Skills: Proven track record running workshops, authoring technical standards, and upskilling engineering teams during delivery.