Staff Analytics Engineer

Role overview

Semantic layer and evaluation

• Build governed Snowflake Semantic Views with logical tables, relationships, facts, dimensions, metrics and synonyms.
• Create evaluation harnesses with golden-question datasets, LLM-as-a-judge scoring and side-by-side release checks.
• Curate verified queries and custom instructions to improve answer accuracy.

Data products and governance

• Build logging, monitoring and lineage, coordinating with data engineering and governance on upstream observability.
• Deliver dynamic Streamlit applications and lightweight data products; modernize Tableau and Power BI dashboards.
• Cross-train BI analysts to maintain domain models and scale a hub-and-spoke model.
• Embed governance and data quality, supporting certification, stewardship and CDE requirements.

Experience and technical qualifications

• Requires 8–12 years in business analytics and data engineering, including at least 1 year building semantic models or LLM-grounded analytics.
• Requires a track record of reusable data products and deep SQL and dimensional or semantic modelling expertise.
• Requires hands-on semantic-layer experience using Snowflake Semantic Views, dbt Semantic Layer, LookML or a similar option.
• Requires hands-on Snowflake experience; Cortex Analyst, Cortex Agents and Cortex Search are desirable, or comparable text-to-SQL and RAG systems.
• Requires Python and analytics-engineering fundamentals across Snowflake dynamic tables, streams, tasks, Snowpark, Git, CI/CD and automated testing.
• Requires production LLM evaluation experience using test datasets and LLM-as-a-judge or programmatic scoring.
• Requires experience with interactive data apps such as Streamlit and enough Tableau and Power BI to modernize reporting.
• AI-assisted coding tools are expected in daily delivery; spec-driven development and Monte Carlo or similar observability tools are pluses.

Collaboration and communication

• Requires strong communication, ability to run BI analyst working sessions, translate ambiguous requests into certified definitions and help peers maintain models.

Benefits

• Benefits listed include medical, dental and vision coverage, paid time off, holidays, two paid volunteer days, an employee stock purchase program and learning opportunities.
• N-able describes its working model as hybrid, based on trust and flexibility.

About N-able