Position Overview
We are looking for a Senior Analytics Engineer to help define and scale analytics across Experity.
This role will own analytical domains and build semantic models, trusted metrics, reusable datasets, and AI-ready data products. The Senior Analytics Engineer will work across analytics, data engineering, architecture, product, and AI teams to translate ambiguous business needs into scalable analytical solutions.
This is not primarily a dashboard development or traditional reporting role. The focus is creating trusted analytical foundations that can be reused across products, teams, applications, and AI experiences.
The ideal candidate combines strong technical execution with analytical judgment. They can independently work through ambiguity, challenge assumptions, make sound modeling decisions, and improve how analytics is delivered.
What You’ll Do
· Own assigned analytical domains from requirements clarification through implementation, testing, deployment, documentation, and production support.
· Design and maintain semantic models representing Experity’s products, business processes, and core business entities.
· Define reusable analytical datasets, governed metrics, and centralized business logic.
· Translate ambiguous business needs into scalable data products rather than one-off analytical solutions.
· Determine appropriate data grain, facts, dimensions, conformed entities, and relationships.
· Make informed decisions about whether business logic belongs in the transformation layer, semantic layer, or consuming application.
· Develop production-quality transformations using SQL, Python, Snowflake, and dbt.
· Establish automated testing, documentation, monitoring, and data-quality controls for owned models.
· Design semantic models, metadata, metric definitions, descriptions, and verified query patterns that enable AI tools and natural-language interfaces to reliably interpret analytical data.
· Use AI-assisted development tools to improve engineering speed, quality, and consistency.
· Partner directly with U.S.-based analytics, engineering, product, and business stakeholders to clarify requirements and identify tradeoffs.
· Proactively raise unclear requirements, data quality concerns, architectural risks, and design decisions.
· Collaborate with data engineers and architects to establish reliable and scalable analytical patterns.
· Lead analytics-engineering onboarding for new products and business domains.
· Identify duplicated logic, inconsistent metric definitions, architectural gaps, and opportunities for automation or reuse.
· Create reusable patterns that can be adopted across domains rather than solving the same problem repeatedly.
· Review analytical designs and code produced by other engineers and contribute to engineering standards and best practices.
· Troubleshoot complex production, performance, and data-quality issues.
What Success Looks Like
· Assigned analytical domains can move from business requirements to production with limited supervision.
· Metrics are trusted and consistent across BI tools, applications, products, and AI experiences.
· Priority domains have reusable, well-documented semantic models.
· Business logic is centralized, testable, version-controlled, and appropriately governed.
· Architectural and data-quality issues are identified before they become downstream reporting problems.
· Analysts spend less time rebuilding foundational logic or reconciling competing metric definitions.
· New products and domains follow reusable patterns rather than starting from scratch.
· Patterns established in one domain are successfully reused across additional domains.
· Junior and mid-level engineers benefit from technical guidance, reviews, and established engineering practices.
Required Qualifications
· 5+ years of…