Senior AI Data Engineer

Senior AI Data Engineer This role has been designed as ‘’Onsite’ with an expectation that you will primarily work from an HPE office.

Hewlett Packard Enterprise is the global edge-to-cloud company advancing the way people live and work. We help companies connect, protect, analyze, and act on their data and applications wherever they live, from edge to cloud, so they can turn insights into outcomes at the speed required to thrive in today’s complex world. Our culture thrives on finding new and better ways to accelerate what’s next. We know varied backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good. If you are looking to stretch and grow your career our culture will embrace you. Open up opportunities with HPE.

HPE Financial services  is where we help organizations create the investment they need for digital transformation, in an innovative and sustainable way. We partner with customers across their entire IT asset portfolio from edge to cloud to end-user. Unique to each client’s aspirations and size, our financial and asset management solutions are anchored by best-in-class tech upcycling services.  Join us redefine what’s next for you.

Summary The Senior Data Scientist role is an individual contributor role that acts as technical subject matter across the full data platform stack - data architecture, transformation design, data quality frameworks, and governance fit for both analytical and AI consumption. The role will work hand in glove with the AI engineers and will play a pivot role in enabling high quality and accuracy data for AI products. This role will also act as a technical mentor for the data engineering team and a trusted partner to the Senior AI & ML Engineer — jointly ensuring that governed, high-quality data reliably powers both reporting and AI use cases.

Technical Leadership & Data Architecture Serve as the data engineering SME — the escalation point for complex platforms, pipeline, and governance decisions across the team and organization. Architect end-to-end data solutions: Design reusable data components, design Lakehouse structures, data vault patterns, semantic layers, and integration of architectures across AI, Analytics and Automation platforms. Define and enforce data engineering standards, pipeline design patterns, naming conventions, and coding best practices; conduct architecture and code reviews. Lead technical discovery for new data initiatives: assess feasibility, design solution approaches, and produce architecture documentation for stakeholder alignment. Mentor and upskill the Technical Data Engineer through structured knowledge transfer, pair programming, and design reviews. Advanced Data Transformation & Pipeline Engineering Design and deliver complex, production-grade ELT/ETL pipelines using Databricks (Delta Live Tables, PySpark, Unity Catalog) and Microsoft Fabric (Dataflows Gen2, Notebooks, Data Factory). Architect reusable, parameterized pipeline frameworks that the wider team can adopt — reducing one-off scripting and increasing delivery velocity. Define and implement advanced transformation patterns: multi-hop Delta Lake pipelines, SCD Type 2/6, event-driven streaming ingestion, and late-arriving data handling. Optimize pipeline performance at scale — partitioning strategy, Z-ordering, liquid clustering, broadcast joins, and cost-based query planning in Spark. Data Quality Strategy & Governance Leadership Define the AI products data quality framework — establish DQ dimensions, thresholds, escalation paths, and remediation SLAs across all critical datasets. Drive business glossary completeness, lineage documentation, data stewardship workflow design, and policy management. Implement automated data quality validation at scale. Integrate DQ gates into CI/CD pipeline deployments. Act as the data