Crosstrain BI analysts in each domain to tune, extend and maintain their domain models, building the hub-and-spoke that scales this beyond a single team.
Embed governance and data quality by default, partnering with the Data Governance team members to meet certification, stewardship, and CDE requirements.
What You'll Bring
Deep SQL and dimensional/semantic modeling expertise (facts, dimensions, metrics, conformed entities), with hands-on experience building a semantic layer in Snowflake Semantic Views, dbt Semantic Layer, LookML, or similar.
Hands-on Snowflake experience, ideally including Cortex Analyst, Cortex Agents, and Cortex Search, or comparable text-to-SQL and RAG systems, and a working grasp of why grounding LLMs in governed semantics beats pointing them at raw schema.
Strong Python and analytics-engineering fundamentals across Snowflake dynamic tables, streams, tasks, Snowpark, Git workflows, CI/CD, and automated testing, with the discipline to ship documented, maintainable, reviewable code.
Fluency with AI-assisted coding (Snowflake Cortex Code, Claude Code, or similar) as a daily driver for delivery; spec-driven development experience is a plus.
Proven experience evaluating LLM applications in production: test datasets, LLM-as-a-judge or programmatic scoring, and using the results to drive iteration. Familiarity with Monte Carlo or similar data observability platforms is a plus.
Experience building dynamic, interactive data apps (Streamlit or similar) on top of a semantic layer, plus enough Tableau and Power BI to migrate what matters and modernize the reporting estate.
Strong communication skills with a knack for cross-training peers, able to run working sessions with BI analysts, translate ambiguous business asks into precise, certified definitions, and help peers tune the model confidently.
8+ years in analytics or data engineering, with at least 1 year building semantic models or LLM-grounded analytics, and a track record of shipping data products that other teams reuse.
Purple Perks
Group Medical , Personal Accident & Term life coverage
Generous PTO and observed holidays
2 Paid VoluNteer Days per year
Employee Stock Purchase Program
FuN-raising opportunities as part of our giving program
N-ablite Learning – custom learning experience as part of our investment in you
The Way We Work – our hybrid working model based on trust and flexibility
About N-able
At N-able, our mission is to protect businesses against evolving cyberthreats with an end-to-end cyber resilience platform to manage, secure, and recover. Our scalable technology infrastructure includes AI-powered capabilities, market-leading third-party integrations, and the flexibility to employ technologies of choice—to transform workflows and deliver critical security outcomes. Our partner-first approach combines our products with experts, training, and peer-led events that empower our customers to be secure, resilient, and successful.
#LI #AS
#LI # Hybrid
Design and build governed Snowflake Semantic Views (logical tables, relationships, facts, dimensions, metrics, synonyms) as the shared vocabulary AI agents use to answer business questions.
Stand up the evaluation harness for the semantic and agentic layer, including golden-question datasets, LLM-as-a-judge scoring, and side-by-side runs that gate every release.
Build logging, monitoring, and lineage across the layer; partner with data engineering and governance team members on observability for upstream pipelines and data products.
Curate a verified query repository and custom instructions that raise answer accuracy over time.
Ship dynamic Streamlit apps and lightweight data products on top of the semantic layer using AI-assisted coding, modernizing legacy Tableau and Power BI dashboards into faster, interactive experiences.
Crosstrain BI analysts in each domain to tune, extend and maintain their domain models, building the hub-and-spoke that scales this beyond a single team.
Embed governance and data quality by default, partnering with the Data Governance team members to meet certification, stewardship, and CDE requirements.
Deep SQL and dimensional/semantic modeling expertise (facts, dimensions, metrics, conformed entities), with hands-on experience building a semantic layer in Snowflake Semantic Views, dbt Semantic Layer, LookML, or similar.
Hands-on Snowflake experience, ideally including Cortex Analyst, Cortex Agents, and Cortex Search, or comparable text-to-SQL and RAG systems, and a working grasp of why grounding LLMs in governed semantics beats pointing them at raw schema.
Strong Python and analytics-engineering fundamentals across Snowflake dynamic tables, streams, tasks, Snowpark, Git workflows, CI/CD, and automated testing, with the discipline to ship documented, maintainable, reviewable code.
Fluency with AI-assisted coding (Snowflake Cortex Code, Claude Code, or similar) as a daily driver for delivery; spec-driven development experience is a plus.
Proven experience evaluating LLM applications in production: test datasets, LLM-as-a-judge or programmatic scoring, and using the results to drive iteration. Familiarity with Monte Carlo or similar data observability platforms is a plus.
Experience building dynamic, interactive data apps (Streamlit or similar) on top of a semantic layer, plus enough Tableau and Power BI to migrate what matters and modernize the reporting estate.
Strong communication skills with a knack for cross-training peers, able to run working sessions with BI analysts, translate ambiguous business asks into precise, certified definitions, and help peers tune the model confidently.
8+ years in analytics or data engineering, with at least 1 year building semantic models or LLM-grounded analytics, and a track record of shipping data products that other teams reuse.
