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As an AI Data Engineer, you will build domain data products and pipelines that are ready for AI consumption and certified from day one. You will shape retrieval-ready, semantically annotated, contract-governed data assets that power pod use cases and enable enterprise reuse. The role blends data engineering with AI-focused governance, quality scoring, and agent-enabled curation to scale data delivery across the platform. You will collaborate with data owners, stewards, and IT to ground pod data in reliable sources and drive measurable improvements in data-to-build time. This is a hands-on, cross-functional role at the intersection of data engineering, AI, and governance.
Responsabilidades- Build domain data products and pipelines to the data plane's certification standards (semantic definition, contracts, entitlement metadata, lineage).
- Ensure data quality and model-readiness with defined quality scoring and signals for evaluation pipelines.
- Collaborate with domain data owners and stewards to ground retrieval on steward-validated sources.
- Develop retrieval foundations (structured/unstructured grounding, vector/graph assets) on the platform estate.
- Apply AI-built curation (metadata generation, entity resolution, content classification) and contribute outputs to the data registry.
- Create reusable data assets for certification and enterprise reuse, handing them to the data plane for promotion.
- Work with business teams and IT to model domain data, and design data collection frameworks for large-scale data ingestion.
- Establish data set processes, data structures, and storage strategies across multiple sources and formats.
- Advise on design standards and assurance processes to ensure data connections and storage meet requirements.
- Develop data tools for analytics and data scientist teams; prepare data for predictive/prescriptive modeling; identify automation opportunities.
- Review requirements and recommend changes to systems and storage to support evolving needs.
Requisitos principales- Strong data engineering with a focus on AI-ready data products (beyond traditional warehouses).
- Hands-on experience with the platform estate: Microsoft Fabric, Snowflake, vector and graph stores.
- Experience with RAG data foundations: chunking, embeddings, hybrid retrieval, and agent-related failure modes.
- Ability to work in a business domain: interview stewards, interpret pipeline outputs, and value domain knowledge.
- Curiosity about AI, its potential and pitfalls; continuous learner in a fast-moving field.
- Excellent communication and influencing skills; able to engage domain experts and stakeholders.
- Ability to generalize and design for second consumers of assets, not just first. xkdbapo
- Bachelor's or Master's Degree or equivalent; 8+ years of relevant experience.
- excellent communication
- ability to influence
- curiosity about AI and its pitfalls
- AI-ready data product engineering
- semantic data definitions and contracts
- data lineage and certification requirements