Job Description THE ROLE Are you a hands-on Data Engineering lead looking for your next challenge in an innovative environment? Join our innovative, growing scale-up where you will lead a team to design, build, and grow the core data function, setting the foundaion for our next generation of business growth? We seek a highly experienced Data Architecture & Strategy Lead who possesses deep technical fluency and architectural leadership skills. This is a hands-on, execution-focused role chartered with solving our immediate structural data deficiencies in business operations. You will personally design, implement, and stabilise the core data framework necessary for future scaling, robust governance, and the successful deployment of AI-driven automation projects. KEY RESPONSIBILITIES Architectural Leadership & Execution Architectural Discernment: Establish a clear "Build vs. Buy" framework and determine when to leverage specialized stores (Vector, Graph) vs. traditional relational databases based on cost, latency, and ROI. Automation Foundation: Design data ingestion and serving layers that account for the unique latency requirements of RAG and Agentic workflows compared to traditional BI. Legacy Re-architecture: Identify and re-platform inefficient, high-cost ETL/ELT processes to improve data freshness and reduce infrastructure spend. Data Governance and Quality Implementation Establish Data Contracts: Move beyond "cleanup" by negotiating and implementing Data Contracts with Product and Engineering teams to ensure quality is owned at the point of creation. Metric Standardization & Conflict Resolution: Act as the arbiter for metric definitions (e.g., CAC, Default Rates) to resolve departmental discrepancies and ensure a single source of truth. Data Observability & Traceability: Implement monitoring and "time-travel" lineage to trace AI outputs (or hallucinations) back to specific data snapshots for debugging and reliability. Budget and Vendor Ownership AI FinOps: Manage the unpredictable cost structures of AI-related data platforms, including token usage, vector compute, and API-call forecasting.
