Data Engineer Senior Consultant

At Allstate, great things happen when our people work together to protect families and their belongings from life's uncertainties. And for more than 90 years, our innovative drive has kept us a step ahead of our customers' evolving needs. From advocating for seat belts, air bags and graduated driving laws, to being an industry leader in pricing sophistication, telematics, and, more recently, device and identity protection.

Responsible for the design, development, and maintenance of the data pipelines, models, and architecture that move data from its source through to a marketing semantic layer, and for ensuring that data is structured, reliable, and ready for use. This role is the accountable technical owner of that semantic layer, built on large centralized semantic models that sit on top of the raw data tables and turn them into a single, trusted source of truth, with every metric defined once and defined consistently.

In practical terms, it connects marketing spend to quotes, to policies, and to customer lifetime value, and it is what marketing leadership reads when deciding where campaign dollars go. Built entirely on Microsoft Fabric, this is a hands‑on build-and‑own role: the person in this seat decides how the semantic layer is structured, defends those decisions to the teams that depend on them, and is the escalation point when the model and the business disagree about what a number means.

This is not a report building role and it is not a request queue.

• Design, develop, and maintain the data pipelines and backend queries that populate the semantic layer, maintaining separation between raw, clean, and reporting layers so business logic lives where it belongs and not in the reporting layer.
• Own the data modelling and architecture of the semantic layer end to end: structure, relationships, storage mode, and refresh behaviour, including the architectural decisions behind each.
• Author, maintain, and performance‑tune the DAX measure layer, including the measure patterns downstream report builders depend on.
• Implement and maintain data frameworks and architectures that keep the platform's data consistent, accurate, and ready for use.
• Produce and maintain model documentation as a first‑class deliverable, since the semantic layer serves report builders, analysts, and stakeholders who did not build it.
• Combine, optimise, and manage multiple upstream data sources, partnering with the business intelligence and data engineering teams on source changes, deployment practices, and promotion of work from development into production.
• Serve as the technical point of contact when downstream consumers report the model is wrong, and own the investigation through to root cause and fix.
• Perform on‑demand analysis of complex data to identify strategic opportunities and efficiencies and to keep key business metrics accurate and trustworthy.
• Mentor apprentice team members working on model quality assurance and report migration, and review their work.
• Contribute to the department's applied AI efforts, including agent‑based access to model documentation and semantic models.

• Strong SQL, including T‑SQL, sufficient to write, review, and debug production analytical queries, and to recognise when generated or inherited code is incorrect rather than merely plausible.
• Python, sufficient to build and maintain data transformation notebooks.
• DAX, including measure authoring, performance tuning, and evaluation context.
• Demonstrated experience owning a large, centralized semantic model or semantic layer, or an equivalent analytical data product, including responsibility for its structure rather than only its contents.
• Experience working with data engineering and business intelligence partners on shared infrastructure.
• Ability to make and defend architectural decisions independently, and to explain the tradeoffs to both technical and business audiences.

• Microsoft Fabric experience (pipelines, lake houses, warehouse, Direct Lake semantic models). Fabric experience is rare and learnable on the job by a strong candidate, so this is preferred rather than…