Senior Analytics Engineer - Decision Intelligence

<h3>Accountabilities</h3><li>
<strong>Build and maintain the gold data layer:</strong>Develop conformed data models for customers, accounts, projects, products, and contracts, reconciling identities and relationships across CRM, application, payment, and billing systems.</li><li>
<strong>Own the semantic and metrics layer:</strong>Define, document, version, and maintain standardized business metrics so dashboards, analytical notebooks, and AI agents consistently produce the same trusted results.</li><li>
<strong>Develop AI-ready data interfaces:</strong>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.</li><li>
<strong>Establish data quality and correctness controls:</strong>Implement automated assertions for data grain, uniqueness, and cross-system reconciliation, ensuring issues are detected early and routed to clearly designated owners.</li><li>
<strong>Evaluate AI-generated analytical outputs:</strong>Create evaluation frameworks that compare agent responses against verified answers, measuring accuracy, appropriate refusal, and confidently incorrect responses as distinct performance indicators.</li><li>
<strong>Translate business questions into reusable data products:</strong>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.</li><li>
<strong>Drive technical standards and team foundations:</strong>Help establish engineering practices, documentation, workflows, and quality standards for a new team, taking ownership of solutions in an evolving technical environment.</li><li>
<strong>Integrate AI into everyday engineering:</strong>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.</li><h2>Requirements</h2><li>
<strong>Strong SQL and Python expertise:</strong>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.</li><li>
<strong>Ownership of semantic or metrics layers:</strong>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.</li><li>
<strong>Advanced data modeling judgment:</strong>A strong understanding of data grain, entity relationships, keys, uniqueness, and reconciliation, with the ability to distinguish technical data defects from unresolved business definitions.</li><li>
<strong>Demonstrated AI-native working practices:</strong>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.</li><li>
<strong>Experience with production data environments:</strong>The ability to deliver dependable analytical models and tools that operate on real production data, with appropriate testing, monitoring, and quality controls.</li><li>
<strong>Comfort with ambiguity and early-stage environments:</strong>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.</li><li>
<strong>Strong communication and documentation skills:</strong>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.</li><li>
<strong>Collaborative problem-solving:</strong>Experience working with cross-functional stakeholders to understand business needs, resolve conflicting definitions, and translate analytical questions into scalable, reusable solutions.</li><li>
<strong>Adaptability and continuous learning:</strong>A willingness to experiment, respond quickly to changing priorities, adopt emerging technologies, and continuously improve technical practices in a rapidly evolving AI-focused environment.</li><h2>Additional experience that would be an advantage:</h2><li>Familiarity with usage-based or consumption-based business models, particularly where committed, consumed, invoiced, and recognized revenue require distinct definitions and reconciliation.</li><li>Experience with real-time billing, metering, or consumption-tracking systems.</li><li>Knowledge of lakehouse architectures and technologies such as Apache Iceberg, Amazon Athena, Trino, or similar platforms.</li><li>Contributions to open-source data engineering, analytics, or AI projects.</li><h2>Benefits</h2><li>
<strong>Competitive base salary:</strong>For Tier 1 locations, including San Francisco, New York City, and Seattle, the annual base salary range is<strong>$200,000–$240,000</strong>. For Tier 2 locations, the annual base salary range is<strong>$185,000–$225,000</strong>.</li><li>
<strong>Equity opportunities:</strong>Equity compensation is offered as part of the compensation package.</li><li>
<strong>Additional compensation:</strong>Tier 2 positions include bonus opportunities. Bonus eligibility and terms are subject to the applicable compensation plan.</li><li>
<strong>Remote work:</strong>A fully remote position based in the United States, offering flexibility in where you work.</li><li>
<strong>Full-time employment:</strong>A full-time opportunity with meaningful technical ownership and the ability to influence foundational data and AI capabilities.</li><li>
<strong>High-impact technical work:</strong>The opportunity to build core decision intelligence infrastructure that supports business decisions and AI-driven analytical workflows.</li><li>
<strong>Autonomy and ownership:</strong>Join an early-stage team where you can shape technical standards, define processes, and influence architectural decisions.</li><li>
<strong>Innovation-focused environment:</strong>Work in an AI-first culture that encourages experimentation, continuous learning, and the practical adoption of emerging AI capabilities.</li>