Senior Business Intelligence Analytics Engineer

What you'll do at Jamf:

The Senior BI Analytics Engineer is a core technical role in Jamf’s data organisation. You’ll own the design and delivery of trusted data models across the Jamf Data Platform, from source-aligned staging layers through to the business-facing marts and semantic views that power BI, financial reporting, and product analytics. This is an end-to-end role: you think in systems, not just queries.

You’ll bridge the gap between raw data from backend services and the decisions Jamf needs to make faster, working closely with data engineers, analysts, data scientists, and business stakeholders to ensure data is accurate, well-defined, and genuinely useful. As AI-powered analytics becomes central to how Jamf operates, you’ll also play a key role in building the semantic foundation that enables natural language querying through tools like Snowflake Cortex Analyst. You’ll raise the bar on data quality, drive best practices across the discipline, and help make the Jamf Data Platform a genuine competitive asset.

What you can expect to do in this role:

• Design and build robust downstream data models using the Trusted Data Development workflow, covering range of analytics and integration use cases.
• Design and maintain Snowflake semantic views with rich metadata annotation, business definitions, entity relationships, and column-level descriptions, enabling reliable AI-driven and natural language querying via tools such as Snowflake Cortex Analyst.
• Partner with Data Platform and AI Architecture teams to build solutions that bridge structured analytics with natural language interfaces, making Snowflake data reliably queryable by AI agents and business users.
• Set and uphold engineering standards writing maintainable, high-scale SQL and dbt code, leading code reviews, and acting as the code owner and BI reviewer for specific schemas.
• Champion data quality and trusted data models implementing testing frameworks, driving data profiling, and partnering cross-functionally to resolve quality issues at their source.
• Own one or more business stakeholder relationships, translating requirements into well-integrated data designs and ensuring raw data is accurately interpreted across business units, drawing on depth in at least two major data domains such as marketing, sales, finance product, or engineering.
• Drive Data Platform optimisation identifying opportunities to reduce cost and complexity, contributing to the Data Catalog, and leading prioritisation of data governance issues.
• Upskill analysts and business partners through code pairing, training, and documentation scaling data acumen and enabling self-service analytics across the organisation.
• Lead multiple analytics projects from beginning to operationalization