Position Summary The Analytics Engineer is responsible for owning the design, build, and ongoing maintenance of data products in the MART layer of the firm’s data platform. This role creates and evolves foundational and business data products from governed underlying data, applying reusable business logic, documentation, testing, and access controls so data can be consumed consistently across the enterprise. The ideal candidate also authors and maintains semantic models that make trusted data easier to use for AI experiences, dashboards, reporting, and downstream integrations into operational platforms.

Success in this role requires strong technical depth, a product mindset, and close partnership with engineering, BI, analytics, and business stakeholders. This is a hybrid role with 3 days/week onsite in St. Louis.

Responsibilities

Own the design, development, and maintenance of MART-layer foundational and business data products built from curated enterprise data.

Requirements

into reusable data products, metrics, and transformation logic that support consistent consumption across teams and platforms. Build and maintain semantic models with rich metadata, business definitions, and synonyms to enable trusted self-service analytics and AI consumption. Develop models that support multiple consumption patterns, including dashboards, reports, AI agents, APIs, file delivery, and operational system integrations.

Apply complex business logic in a governed, version-controlled manner using modern transformation tooling and software engineering best practices. Partner with data engineers, BI engineers, analysts, architects, and business stakeholders to define product intent, validate logic, and prioritize enhancements. Ensure data product quality through testing, monitoring, reconciliation, and issue resolution across upstream data, transformations, and downstream consumption.

Document data products, semantic models, lineage, and usage guidance to improve transparency, stewardship, and user adoption. Support governed access patterns through metadata, role-based access considerations, and alignment with enterprise data governance standards. Continuously improve MART-layer patterns, semantic model design, and consumer enablement based on platform evolution and business needs.

Required Skills Strong experience building analytics-ready data models and reusable business logic in modern cloud data platforms such as Snowflake. Hands-on expertise with DBT or similar transformation frameworks, including modular modeling, testing, documentation, and version control. Proven ability to design and maintain semantic models, metrics, dimensions, and business definitions for consistent downstream consumption.

Strong SQL skills and experience optimizing transformation logic for performance, scalability, and maintainability. Experience supporting multiple data consumption patterns, including BI dashboards, AI use cases, reporting, APIs, file delivery, and operational integrations. Understanding of data governance, metadata, lineage, role-based access, and quality controls in enterprise data platforms.

Ability to partner effectively with cross-functional stakeholders to translate business needs into scalable, trusted data products. Strong written and verbal communication skills with the ability to document logic, definitions, assumptions, and usage guidance clearly.

Qualifications

Bachelor’s degree in Computer Science, Information Systems, Data Analytics, Engineering, or a related field, or equivalent practical experience. 5+ years of experience in analytics engineering, data engineering, BI engineering, or a closely related data role. Experience building governed data models and data products in enterprise environments with multiple downstream consumers.

Demonstrated experience applying software engineering best practices to analytics workflows, including Git-based development, testing, and deployment discipline.