Data Engineer

Department: Information Technology 500003

Employment Type: Full Time

Location: Sterling Heights

Reporting To: Matt Knutson

Backed by a tireless commitment to high quality, innovative culinary offerings and unparalleled client and guest services, Continental consistently ranks among the top contract dining and refreshment providers in the nation according to Food Management Magazine, the industry’s source for food service news and trends.

Our Mission:
Delight our guest. Every meal. Every day.

Our Core Values:
Exceptional – We recruit and retain phenomenal people who consistently go the extra mile to deliver results that lead the industry.
Hospitality – We are tirelessly committed to providing the highest quality food, beverages, and service by anticipating our guests’ needs so they can count on memorable experiences every time.
Collaboration – We prioritize working together with a shared vision and effective & transparent communication, we unite as one team to achieve remarkable experiences.
Responsibility – we hold ourselves accountable to be there for our team and clients, to deliver what we say we will, when we say we will, and check in on progress along the way.
Innovation – We are forward‑thinkers always looking for new processes, technologies, and techniques to increase efficiencies and scalability to improve our services.

The Data Engineer is a hands‑on technical contributor who plays a foundational role in building the data infrastructure that powers Continental’s analytics, reporting, and AI initiatives. This person will work at the intersection of our business systems and our growing data platform—designing and maintaining the pipelines, models, and processes that bring together data from across the organization into a reliable, unified foundation.

We’re looking for someone who is technically strong, intellectually curious, and energized by working on problems that have a real and visible impact on how the business operates and grows.

• Design, build, and maintain scalable cloud‑native data pipelines—ETL/ELT, event‑driven and serverless patterns using Azure Functions and related services—that ingest, transform, and load data from Continental’s diverse source systems including ERP, POS, HRIS, and finance platforms into a centralized lake or data warehouse.
• Develop and maintain data models within Microsoft Fabric and Azure that support analytics, business intelligence, and emerging AI use cases, ensuring data is structured for both performance and usability.
• Integrate data from disparate systems across Continental’s business units into a unified enterprise data model, resolving inconsistencies and establishing a single source of truth for key business metrics.
• Implement and enforce data quality, validation, and monitoring practices that ensure downstream consumers—analysts, dashboards, and AI models—can trust the data they are working with.
• Collaborate closely with the Business Analyst and IT AI & Data Manager to understand reporting and analytics requirements, translating them into efficient, well‑documented data solutions.
• Design and implement AI‑augmented data engineering solutions—including AI agents, automated data quality processes, and intelligent pipeline components—that drive efficiency across Continental’s data platform.
• Build and optimize semantic models and datasets that power PowerBI dashboards and reports, working with analysts to ensure visualizations are backed by performant, accurate data.
• Contribute to data governance efforts by maintaining data dictionaries, pipeline documentation, and lineage tracking so the team can understand, trust, and build on the work being done.
• Monitor pipeline health, troubleshoot data failures, and proactively address issues before they impact reporting or downstream business processes.
• Stay current on developments in cloud data engineering, AI tooling, and the Microsoft data ecosystem, bringing new ideas and approaches to the team as the platform continues to mature.
• Support M&A and new business onboarding by connecting new source systems into existing pipelines and data models with minimal disruption to…