Data Engineer Senior Consultant

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 modeling and architecture of the semantic layer end to end: structure, relationships, storage mode, and refresh behavior, 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, optimize, 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 recognize 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, lakehouses, warehouse, Direct Lake semantic models). Fabric experience is rare and learnable on the job by a strong candidate, so this is preferred rather than required.
• Tabular Editor.
• Marketing measurement or attribution background: how spend connects to quotes, binds, policies, and lifetime value.
• Insurance industry experience.
• Experience building or operating data quality frameworks.

Skills DAX, Data Analytics, Data Modeling, Data Pipelines, Data Quality, Large Semantic Models, Microsoft Fabric, Microsoft Power BI, Python, Semantic Data Modeling, Semantic Layer, SQL, Technical Documentation, Tabular Editor

Experience

• 3 or more years of experience (Preferred), Big Data Analytics, Business Intelligence (BI), Data Analytics, Data Engineering, Data Modeling, Data Pipelines, Data Quality, Data Transformation, Python (Programming Language), Semantic Modeling, Structured Query Language (SQL)

Remote Hiring Remotely in US 70K-121K Annually Senior level Remote Hiring Remotely in US 70K-121K Annually Senior level Own the design, development, maintenance, and performance of Microsoft Fabric data pipelines, centralized semantic models, DAX measures, and documentation. Ensure data quality, consistency, and trusted marketing metrics across sources. Partner with data engineering and BI teams on deployments and production issues, investigate model discrepancies, perform analytical work, mentor apprentices, and contribute to applied AI initiatives. The summary above was generated by AI

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., Compensation offered for this role is 70,100.00 - 121,475.00 annually and is based on experience and qualifications.

The candidate(s) offered this position will be required to submit to a background investigation.

Joining our team isn’t just a job - it’s an opportunity. One that takes your skills and pushes them to the next level. One that encourages you to challenge the status quo. One where you can shape the future of protection while supporting causes that mean the most to you. Joining our team means being part of something bigger - a winning team making a meaningful impact.

Allstate generally does not sponsor individuals for employment-based visas for this position.

Effective July 1, 2014, under Indiana House Enrolled Act (HEA) 1242, it is against public policy of the State of Indiana and a discriminatory practice for an employer to discriminate against a prospective employee on the basis of status as a veteran by refusing to employ an applicant on the basis that they are a veteran of the armed forces of the United States, a member of the Indiana National Guard or a member of a reserve component.