Senior Analytics Engineer - Decision Intelligence

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Analytics Engineer – Decision Intelligence based in the United States. Overview We are seeking a Senior Analytics Engineer to build a trusted, governed decision intelligence layer that empowers teams and AI agents to make accurate, consistent, data-driven decisions. In this senior individual contributor role, you will transform curated warehouse data into reliable business models, standardized metrics, and AI-ready analytical tools. You will establish a unified view of customers, accounts, products, projects, and contracts across multiple systems. Working at the intersection of data engineering, analytics, and AI, you will define the standards that ensure data accuracy, consistency, and accountability. You will collaborate closely with sales, product, finance, and technical teams to solve complex business questions and create scalable analytical foundations. As an early member of a newly formed team, you will have significant ownership, autonomy, and influence over technical direction. This is an AI-first, fast-paced environment for someone who enjoys experimentation, continuous learning, and building foundational systems from the ground up. Accountabilities - Build and maintain the gold data layer: Develop conformed data models for customers, accounts, projects, products, and contracts, reconciling identities and relationships across CRM, application, payment, and billing systems.

- Own the semantic and metrics layer: Define, document, version, and maintain standardized business metrics so dashboards, analytical notebooks, and AI agents consistently produce the same trusted results. - Develop AI-ready data interfaces: Build tools, permission frameworks, and Model Context Protocol (MCP) interfaces that enable AI agents to explore and query business data safely, accurately, and without introducing unreliable joins or misleading conclusions. - Establish data quality and correctness controls: Implement automated assertions for data grain, uniqueness, and cross-system reconciliation, ensuring issues are detected early and routed to clearly designated owners. - Evaluate AI-generated analytical outputs: Create evaluation frameworks that compare agent responses against verified answers, measuring accuracy, appropriate refusal, and confidently incorrect responses as distinct performance indicators. - Translate business questions into reusable data products: Partner with sales, product, and finance stakeholders to answer complex questions while building durable models and definitions that make future analysis faster and more reliable. - Drive technical standards and team foundations: Help establish engineering practices, documentation, workflows, and quality standards for a new team, taking ownership of solutions in an evolving technical environment. - Integrate AI into everyday engineering: Experiment with advanced AI tools and incorporate them into development, analysis, and problem-solving workflows to improve productivity, quality, and the scope of work delivered. Requirements - Strong SQL and Python expertise: Demonstrated experience writing, deploying, and maintaining production-grade SQL and Python code, with a track record of building reliable systems and detecting failures through proactive monitoring and instrumentation. - Ownership of semantic or metrics layers: Hands-on experience owning tools and frameworks such as dbt, Cube, LookML, or equivalent solutions, including changing established metric definitions, managing versioning, and communicating the impact to stakeholders. - Advanced data modeling judgment: A strong understanding of data grain, entity relationships, keys, uniqueness, and reconciliation, with the ability to distinguish technical data defects from unresolved business definitions.