Senior Analytics Engineer

NetBrain doesn’t have a data warehouse today — different teams work from different numbers, with no standardized reporting layer or single source of truth for the business. We’re hiring a Senior Analytics Engineer to fix that: architecting and standing up our first cloud data warehouse, and building the transformation layer, ELT pipelines, and governance that our analytics function will run on.

You’ll own the technical infrastructure that makes reliable measurement possible — warehouse, pipelines, and transformations — while partnering closely with the business stakeholders who define what we measure and why. This is a foundational build with high visibility: the semantic layer you help stand up is also core to how NetBrain uses AI internally to query and report on its own data.

What You’ll Do:

• Architect and maintain NetBrain’s cloud data warehouse,
• Own warehouse transformations end to end — from raw ingestion through documented, stable tables that downstream teams and tools depend on,
• Design and maintain ELT pipelines that bring raw data from tools across GTM, Finance, and Product into the warehouse,
• Contribute to the technical foundation of our BI tool — semantic layer, metric definitions, and access model — that powers executive reporting and NetBrain’s internal AI use cases,
• Own data quality, governance, and lineage: freshness checks, testing, access controls, and a documented source of truth,
• Build and maintain curated data models for product usage, customer adoption, account health, and go-to-market reporting.

What You Bring:

• 6+ years in analytics engineering, data engineering, or a similar role, with real ownership of production data models,
• Strong SQL and a track record shipping analytical models that other teams depend on,
• Hands-on experience with a modern cloud data warehouse (Snowflake preferred, or comparable platforms like BigQuery),
• Extensive experience with a transformation framework such as dbt or SQLMesh,
• Scripting ability (Python or similar) for ingestion, validation, or automation,
• Experience with a BI or semantic-layer tool (e.g., Omni, Looker, Tableau),
• Sound judgment around data access, governance, and handling of sensitive data.

Nice to Have:

• Experience building the technical foundation for AI-assisted or natural-language reporting — semantic layers built to support LLM or agent-based tools,
• Familiarity with GTM and Customer Success data sources such as Salesforce, Gainsight, or HubSpot,
• Experience applying machine learning or statistical forecasting techniques (e.g., time-series and churn-propensity models) to predict key business metrics such as ARR and customer retention.