SENIOR DATA LEAD (REMOTE)

Salary:  $130,000 - $150,000

We are CDAI—the data and artificial intelligence engine of Compass Group North America. We design and deliver custom, in-house solutions tailored to the unique complexities of food service and hospitality. Our work is grounded in strong data foundations, layered with AI to enhance forecasting, streamline operations, and enable better, faster decision-making across Compass Group. With deep integration into the business and a commitment to white-glove service, CDAI empowers associates, clients, and customers through innovative, future-forward technologies.

Job Summary

At Compass Data & AI (CDAI), we’re transforming how master data fuels enterprise insight and operational excellence across one of the largest foodservice organizations in the world. You’ll join a growing Master Data Management (MDM) and Item/Product Data team that values curiosity, precision, and collaboration with the opportunity to help shape the data foundation for our digital ecosystem.

Our MDM organization is in its early stages of development, and we’re building the foundation of our Item/Product domain. We’re looking for a Senior Data Analyst who’s eager to help shape this capability from the ground up, defining the standards, processes and data structures that will power our enterprise data ecosystem for years to come.

As part of CDAI, you will play a pivotal role in enabling consistent, high-quality product, manufacturer, and distributor data that supports critical business processes across purchasing, operations and digital experiences. This role sits at the heart of our enterprise data strategy, helping to transform how product and supplier data is captured, governed, and leveraged across the organization.

You will serve as a bridge between data engineering, architecture, product management, and business stakeholders, ensuring that data is modeled, governed, and trusted across the ecosystem. The ideal candidate combines deep technical proficiency in SQL, data profiling, and modeling with the ability to understand source system data and processes, connect them to business needs, and translate them into meaningful, well-governed master data that drives measurable business outcomes.

Responsibilities

• Develop a deep understanding of multiple source systems and how their data supports master data and business processes.
• Serve as a subject matter expert and data champion within the product domain, guiding others in navigating and interpreting source and master data effectively.
• Profile, assess, and explore datasets to identify data quality issues, anomalies, and inconsistencies across systems.
• Partner with business stakeholders to translate data needs into clear, actionable data definitions, mappings, and requirements.
• Support and enforce data governance, standards, and normalization practices that improve consistency and trust in enterprise data assets.
• Collaborate with data modelers and database designers to maintain logical and physical data models aligned to master data principles.
• Define and document entities, attributes, relationships, and lineage, with primary responsibility for the logical mapping from sources to master targets.
• Distinguish between master, reference, and transactional data, ensuring each is properly structured and maintained.
• Validate and test master data outcomes to confirm compliance with business and quality rules.
• Conduct root cause analysis on data issues and work with IT and business teams to drive resolution.
• Partner with data engineers to ensure integrations, transformations, and pipelines align with defined data rules and standards.
• Define survivorship rules and golden-record logic for product/manufacturer/distributor entities; design and tune match/merge and deduplication strategies (deterministic + fuzzy)
• Lead source-to-master mapping design reviews and sign-off; maintain the canonical mapping/specs as the contract between source teams and MDM.
• Influence the MDM roadmap with requirements for new attributes, taxonomies, and mastering rules based on downstream consumer needs.

Qualifications

• 7–10 years of experience as a Data Analyst or in a similar analytical/data stewardship capacity.
• Expert-level SQL skills for querying, profiling, and validating data across large, complex datasets.
• Proven ability to explore, assess, and reconcile data across multiple platforms, identifying root causes of quality issues.
• Strong understanding of data modeling, normalization, and entity-relationship design.
• Experience defining and managing master vs. reference vs. transactional data boundaries.
• Demonstrated ability to translate business requirements i