Job Opportunities API

About Indicium AI  Indicium AI is trusted by the world's leading enterprises to deliver AI into production at scale. We are a global AI-native consultancy with proven experience across Financial Services, Energy & Utilities, Healthcare & Life Sciences, Retail & CPG, and Manufacturing. From strategy, to build, to business outcomes, we unlock value from AI with unmatched clarity, speed, and capability. Powered by 600+ AI experts serving 50+ enterprise clients from 5 global locations, we work side-by-side with top partners - including Anthropic, Databricks, AWS, OpenAI, and Microsoft - to deliver modern AI with speed and measurable impact. Opportunity As a Principal Data Engineer at Indicium AI, you will join a team of AI and data practitioners delivering enterprise-grade information architecture for leading organisations across Europe. Working within complex client engagements, with a strong focus on Financial Services, you will lead data modelling and semantic architecture, shape data platform standards, and guide engineering teams in building AI-ready data products. This is an opportunity to operate as a true design authority, driving architectural rigour while making a measurable impact on clients' data strategies from day one. Responsibilities Data Modelling & Architecture Lead and contribute to conceptual, logical, and physical data model design aligned to client architectures, translating business requirements into rigorous data structures in collaboration with engineering and domain teams. Govern and maintain data models in appropriate tooling, enforcing naming conventions, domain standards, and versioning across source, integration, and consumption layers. Define and steward canonical data entities and lineage, and guide teams on the design and effective use of semantic data models, including their application to agentic AI solutions. Semantic & Knowledge Modelling Design and maintain semantic models to support agentic-AI capabilities, integrating semantic layers with business glossaries, data catalogues, and lineage tooling. Help develop ontologies, taxonomies, and controlled vocabularies underpinning enterprise metadata and search, and define semantic modelling standards and patterns to ensure consistent meaning, interoperability, and AI readiness across data products. Guide and govern the creation of data domains aligned with the client's underlying business capabilities. Data Engineering & Platforms Design data product schemas across platforms including Azure, Snowflake, and Databricks, encompassing Delta Lake structures and interoperable patterns, and define architectural guardrails for performance, scalability, security, and cost efficiency. Set design standards and review transformations and pipelines for compliance, embedding data contract standards and data quality frameworks across pipeline outputs. Agentic AI & AI-Assisted Development Leverage Snowflake Cortex, Databricks Genie, or equivalent tooling to help engineering teams deliver natural language query and LLM-augmented data products. Utilise GitHub Copilot, Claude Code, or similar AI coding assistants to accelerate modelling, documentation, and pipeline development. Design and define a set of agentic skills that can be provided to implementation teams, and contribute to the firm's AI Champion network, evaluating emerging capabilities and advising on adoption. Enterprise Architecture & Design Assurance Act as a design authority for data architecture across teams and data domains, partnering with enterprise, security, and risk architecture to ensure data solutions align with broader technology and regulatory standards. Provide early-stage architectural input to initiative discovery and solution design to reduce delivery risk. Governance & Stakeholder Engagement Present architecture decisions to senior stakeholders, architecture