Senior Analytics Engineer - Decision Intelligence

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. Demonstrated AI-native working practices: Regular, sophisticated use of AI tools beyond code completion, with examples of how AI has transformed your approach to engineering, analysis, or problem-solving. You should be comfortable discussing recent AI-driven projects in depth and explaining their practical impact. Experience with production data environments: The ability to deliver dependable analytical models and tools that operate on real production data, with appropriate testing, monitoring, and quality controls. Comfort with ambiguity and early-stage environments: A proactive, independent mindset and the ability to define problems, establish processes, and make sound technical decisions without relying on a fully developed roadmap or established ticketing system. Strong communication and documentation skills: The ability to write clear metric definitions, data contracts, technical specifications, and explanations that other teams can understand and trust without having to reproduce the underlying analysis. Collaborative problem-solving: Experience working with cross-functional stakeholders to understand business needs, resolve conflicting definitions, and translate analytical questions into scalable, reusable solutions. Adaptability and continuous learning: A willingness to experiment, respond quickly to changing priorities, adopt emerging technologies, and continuously improve technical practices in a rapidly evolving AI-focused environment. Additional experience that would be an advantage: Familiarity with usage-based or consumption-based business models, particularly where committed, consumed, invoiced, and recognized revenue require distinct definitions and reconciliation. Experience with real-time billing, metering, or consumption-tracking systems. Knowledge of lakehouse architectures and technologies such as Apache Iceberg, Amazon Athena, Trino, or similar platforms. Contributions to open-source data engineering, analytics, or AI projects. Benefits Competitive base salary: For Tier 1 locations, including San Francisco, New York City, and Seattle, the annual base salary range is $200,000–$240,000 . For Tier 2 locations, the annual base salary range is $185,000–$225,000 . Equity opportunities: Equity compensation is offered as part of the compensation package. Additional compensation: Tier 2 positions include bonus opportunities. Bonus eligibility and terms are subject to the applicable compensation plan. Remote work: A fully remote position based in the United States, offering flexibility in where you work. Full-time employment: A full-time opportunity with meaningful technical ownership and the ability to influence foundational data and AI capabilities. High-impact technical work: The opportunity to build core decision intelligence infrastructure that supports business decisions and AI-driven analytical workflows. Autonomy and ownership: Join an early-stage team where you can shape technical standards, define processes, and influence architectural decisions. Innovation-focused environment: Work in an AI-first culture that encourages experimentation, continuous learning, and the practical adoption of emerging AI capabilities.