Senior Associate AI Analytics Enablement

What you'll do

• Build and maintain version-controlled documentation and reusable analytical patterns from historical queries.
• Partner with domain experts to establish a governed semantic layer with human-approved definitions.
• Create libraries of reusable skills, procedures, and business rules for consistent analytics usage.
• Develop benchmark test sets, regression suites, and automated evaluations to measure analytics quality.
• Convert analytics standards into auditable mechanisms like linting rules, unit tests, and CI/CD gates.
• Instrument telemetry and monitoring dashboards to track system performance and reliability over time.
• Support experimentation and measurement for natural-language analytics interfaces regarding accuracy and compliance.
• Translate stakeholder feedback into measurable improvements for platform quality and user outcomes.

What we're looking for

• Proficiency in Python for data manipulation, scripting, and automation using libraries like pandas.
• Strong SQL skills including writing, optimizing, and debugging complex queries with joins, aggregations, and window functions.
• Knowledge of software engineering fundamentals such as data structures, algorithms, and clean coding practices.
• Working proficiency with Git-based version control and code review workflows.
• Experience writing tests and implementing automated quality checks in a CI/CD pipeline.
• Experience building data or analytics tooling in modern data-warehouse environments.
• Ability to define business rules and validation logic and convert them into repeatable, testable mechanisms.
• Hands-on experience using AI coding assistants like GitHub Copilot to accelerate development and testing.
• Familiarity with large language models and modern application frameworks like LangChain (preferred).
• Experience building retrieval-augmented generation workflows and managing retrieval quality for analytics use cases (preferred).
• Exposure to semantic layer patterns and governed metrics/definitions in analytics platforms (preferred).
• Experience designing evaluation frameworks for text-to-SQL or conversational analytics (preferred).
• Experience implementing observability patterns for analytics products including telemetry and monitoring (preferred).
• Experience building reusable "harness" tooling or standardized workflows that scale across teams (preferred).