Principal Data Engineer
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 review boards, and regulatory audiences, and support regulatory compliance through robust lineage, metadata management, and data quality evidence.
Mentor junior architects and engineers within the Information Architecture community of practice.
Requirements (These are || not &&)
At Indicium AI we believe in personal growth and professional development, and are convinced that the perfect candidate is one that continuously learns. Therefore, these requirements are an || and not an &&
Experience: 8+ years in data architecture, data engineering, or information management, with proven enterprise data modelling experience at conceptual, logical, and physical levels.
Education: Bachelor's degree in Computer Science, Information Systems, Data Science, Engineering, or a related discipline.
Data Platforms: Practical Snowflake and Databricks experience, including Cortex AI and Genie; familiarity with Azure data services.
Transformation Tooling: Proven experience with dbt Labs in production environments.
Programming & Integration: Good Python skills
Nice to have
Data Vault 2.1 alongside Kimball/Star-schema modelling
Data mesh, data product ownership and federated governance
Knowledge graph platforms (Neo4j, Neptune, Stardog) or graph query languages
Published thought leadership or community contributions in data or AI
Why Indicium AI
Fast-growing start-up organisation with huge opportunity for career growth
Highly competitive salary package along with company bonus
A hugely collaborative working environment where every person’s viewpoint is considered - a chance to make your mark on the business from day one!
Financially backed business meaning security and support for new initiatives and global market expansion
Pick your own Gear! Macbooks, PCs, Accessories!
Drive your development with a personal learning budget
Benefits
Holiday Entitlement - 25 days holiday plus bank holidays
Learning & Development: 1.500€ training budget + 5 training days
Company Bonus - Discretionary company and personal bonus paid quarterly
Pension Scheme
Choose Your Kit - Select from a range of laptops and accessories
Social Events: meeting - Ups, Squad events, Summer/ Christmas events, etc.
And many others.
