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Senior Analytics Engineer

Level Access LogoLevel Access
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Salary

United States: $92,000 - $100,000 / Canada: CA$86,000 - CA$130,000

Location

Remote

Posted

Today

Level Access is hiring a Senior Analytics Engineer to own the data products that power trusted reporting, business decisions and AI experiences at Level. With an initial focus on GTM and revenue, you will translate commercial data into reliable models, certified metrics and a governed semantic layer that people and AI agents can use consistently.

You will work directly with senior leaders and data champions across the company. Our Senior Data Engineer owns the Databricks platform, ETL pipelines and operations. Your focus is the analytics layer: business modeling in dbt, metric implementation, semantic definitions and business-facing data products. You will partner closely on source requirements and upstream issues.

What you will do

You own the path from raw commercial data to certified metrics and KPIs that a person or an AI agent can act on.

  • Own commercial data models. Design, build and maintain dbt models on Databricks for go-to-market and revenue data. Start with pipeline, bookings and subscriptions, then expand into other business domains as the company grows.
  • Turn business definitions into certified metrics. Own the full catalog of go-to-market, revenue and customer metrics, from bookings and retention to pipeline, forecast, customer health and efficiency. Work with business leaders to drive alignment across each definition and its edge cases. Add new metrics as the business asks new questions.
  • Maintain the semantic layer. Build and maintain Databricks metric views, the data dictionary and model documentation in Unity Catalog, so every certified metric has one definition used everywhere.
  • Make commercial data AI-ready. Curate the catalog, descriptions and verified queries the AI agent reads over MCP and maintain a revenue regression question set that measures agent accuracy as models change.
  • Protect data quality. Apply dbt model contracts and tests to certified models. Define freshness, volume and anomaly checks, maintain model lineage and work with the Senior Data Engineer to resolve upstream issues.
  • Enable predictive analytics. Supply governed revenue, contract and subscription features to machine learning models, alerts and AI agents.
  • Deliver business-facing analytics. Own GTM and revenue forecasting datasets and BI feeds. Translate stakeholder questions into reusable data products and prioritize work with business partners.
  • Raise engineering standards. Review dbt code, establish modeling conventions and mentor peers. Use AI-assisted tools for SQL, tests, and documentation while holding generated work to production standards.

What you bring

These are required. We are hiring for depth in modeling and business judgment.

  • Production modeling. 5+ years building production data models in analytics or data engineering, ideally with a GTM, RevOps or SaaS revenue focus, including at least 3 years in dbt: incremental models, snapshots, macros, tests, documentation and CI with state-based deferred builds.
  • Strong SQL. Databricks and Delta experience is preferred; Snowflake or BigQuery experience transfers.
  • Salesforce fluency. Hands-on knowledge of the Salesforce data model, including accounts, opportunities, opportunity line items, contracts, assets or entitlements, quotes and subscriptions, and field history.
  • SaaS revenue metrics. Able to define and defend ARR and MRR, bookings, renewals, expansion, churn and downsell, net and gross revenue retention, and how double counting happens.
  • Metric ownership. Documented business definitions, resolved conflicting numbers across teams, and secured stakeholder approval before publishing metrics.
  • Data quality discipline. Experience with model contracts, reconciliation, testing strategies and freshness or anomaly monitoring for production analytics models.
  • Engineering discipline. Git, code review, pull requests, CI and promotion through environments, and a habit of validating AI-generated code before it ships.
  • Clear communication. Able to explain modeling decisions and tradeoffs to non-technical stakeholders and turn ambiguous questions into agreed requirements.

Nice to have

  • Unity Catalog governance. Governed tags, attribute-based access control (ABAC) column masks, and lineage.
  • CI/CD for Databricks. Declarative Automation Bundles and Git-based deployment workflows.
  • Semantic layers. dbt Semantic Layer (MetricFlow), Cube, Looker (LookML), or Databricks metric views.
  • Agent-facing data tools. MCP servers, verified queries, and agent evaluation datasets.
  • Feature engineering. Model-ready features for churn or propensity models.
  • BI and dashboards. Power BI or Databricks AI/BI dashboards.

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