unity-advisory - Advisory Data Architect
Requirements
• Significant experience designing and delivering data architectures for production systems, with evidence that your designs have shipped and run - not remained on paper. • Deep expertise in data modelling, warehouse/lakehouse design, and data governance. • Hands-on background in SQL and Python, and fluency with at least one major cloud provider (AWS or Azure) and modern data platforms (e.g. Snowflake, Databricks). • System-level judgement on how data architecture shapes AI outcomes: designing governed sources for RAG, structuring data for retrieval and orchestration, and deciding where structured outputs and tool use fit. • Ability to set standards and lead engineers while remaining close to implementation. • Strong stakeholder skills, including engaging senior client sponsors and explaining architectural trade-offs clearly. • Strong Preference • Experience architecting the data layer behind AI or analytics products in production. • Informed views on the trade-offs between fine-tuning, RAG, and long-context approaches, and how each changes data architecture requirements, including context window economics and prompt caching. • Familiarity with infrastructure as code, CI/CD, and observability for data platforms. • Awareness of MCP and tool-orchestration concepts and their implications for composable, data-driven AI systems. • Fluent use of AI-assisted development tools (e.g. Claude Code, Cursor), with a clear view of where they help and where human design judgement must lead. • Security and compliance experience (e.g. ISO 27001 alignment, access control design). • Consulting or advisory background, owning problems end-to-end rather than a single workstream. • Evidence of self-directed building — side projects, internal tools, or startups. • Exposure to finance data, PE/M&A, or portfolio-company environments. • Relevant certifications (e.g. AWS/Azure architect-level credentials).
Responsibilities
• Define target-state data architectures for clients — models, storage, pipelines, and access patterns — balancing rigour with delivery realism. • Establish data modelling standards and curated domain layers across the a variety of systems (e.g. ERP, CRM, Manufacturing) and data sources, with end-to-end lineage and provenance sufficient for audit and controls. • Map and integrate finance source systems (ERP, EPM, sub-ledgers, and consolidation tools) into governed, analytics-ready models. • Design the finance data foundations for applied AI/ML: governed retrieval sources for RAG, feature stores, and the architecture that keeps AI systems fed with trustworthy, reconciled finance data. • Set patterns for secure, compliant finance data platforms — access control, data sensitivity, segregation of duties, isolation, and alignment to standards such as ISO 27001. • Provide architectural direction for cloud data platforms, infrastructure as code, CI/CD, and observability, ensuring designs are cost-aware and operable. • Define the data lineage and controls that support responsible production AI — monitoring, confidence scoring, and human-in-the-loop review. • Translate ambiguous business and client problems into clear architectural decisions and scoped roadmaps under delivery pressure. • Own key technical trade-offs and communicate them clearly to engineers, product stakeholders, and senior client sponsors. • Mentor data engineers, raise the technical bar across the team, and contribute reusable patterns, reference architectures, and accelerators.
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