About the company
- 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.
Responsibilities
- 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.
- Establish data quality and correctness controls: implement automated assertions for data grain, uniqueness, and cross-system reconciliation.
- Evaluate AI-generated analytical outputs: create evaluation frameworks that compare agent responses against verified answers.
- Translate business questions into reusable data products: partner with sales, product, and finance stakeholders to answer complex questions while building durable models.
- Drive technical standards and team foundations: help establish engineering practices, documentation, workflows, and quality standards for a new team.
- Integrate AI into everyday engineering: experiment with advanced AI tools and incorporate them into development, analysis, and problem-solving workflows.
Requirements
- Strong SQL and Python expertise: demonstrated experience writing, deploying, and maintaining production-grade SQL and Python code.
- Ownership of semantic or metrics layers: hands-on experience owning tools and frameworks such as dbt, Cube, LookML, or equivalent solutions.
- Advanced data modeling judgment: strong understanding of data grain, entity relationships, keys, uniqueness, and reconciliation.
- Demonstrated AI-native working practices: regular, sophisticated use of AI tools beyond code completion.
- Experience with production data environments: ability to deliver dependable analytical models and tools that operate on real production data.
- Comfort with ambiguity and early-stage environments: proactive, independent mindset.
- Strong communication and documentation skills: ability to write clear metric definitions, data contracts, technical specifications.
- Collaborative problem-solving: experience working with cross-functional stakeholders.
- Adaptability and continuous learning: willingness to experiment, respond quickly to changing priorities.
Additional experience that would be an advantage
- Familiarity with usage-based or consumption-based business models.
- 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.