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Senior Analytics Engineer

Canada

The annual base salary hiring range for this position is $92,000 - $100,000 for employees located in the United States and CA$86,000 - CA$130,000 for employees located in Canada. · Full-time

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Job details

Pay: The annual base salary hiring range for this position is $92,000 - $100,000 for employees located in the United States and CA$86,000 - CA$130,000 for employees located in Canada.
Job type: Full-time
Posting language: English
Posted: Nov 4, 2026

Original job description

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.

Application Process

If you thrive in a fast paced environment and you are eager to make a significant impact in the field of accessibility, we would love to hear from you! Interested in immediate consideration? Please submit your resume to be considered for this role. The anticipated application window is 40 days from the date job is posted. However, factors such as candidate flow and business necessity may require us to shorten or extend the application window. This position is a current open vacancy. Candidates who are selected for an interview will be notified of the hiring decision within 45 days following their final interview. Level Access is committed to workforce diversity and is an Equal Opportunity Employer. We are dedicated to providing reasonable accommodations throughout the hiring process. Level Access uses artificial intelligence (AI) tools to support and enhance our recruitment and hiring processes. These tools may assist with activities such as résumé screening, skills assessments, interview scheduling, and communication These tools are designed to assist our recruiters and do not make final hiring decisions. All AI-assisted processes are subject to human review and candidate information is handled in accordance with our Privacy Policy and applicable regulations. To learn more about how we collect and use personal data, please review our Privacy Statement (https://www.levelaccess.com/privacy-statement) and our Trust Center (https://trust.levelaccess.com).

Compensation and Benefits

This is a full time position. The annual base salary hiring range for this position is $92,000 - $100,000 for employees located in the United States and CA$86,000 - CA$130,000 for employees located in Canada. The salary range reflects expected base pay for this role at the time of posting. The compensation package offered will be determined by factors such as demonstrated experience, skills, and internal equity. Expected base pay hiring ranges for candidates outside of the United States and Canada will be reviewed during the interview process, if selected. This position is eligible for incentive compensation in the form of a company bonus plan. Level Access is committed to supporting our global team with comprehensive and inclusive benefits. For employees in the United States and Canada, our offerings include medical, dental, and vision insurance, life and disability coverage (STD/LTD), and a retirement match. We also prioritize well-being and growth through flexible paid time off, paid parental leave, a remote work stipend, and professional development reimbursement. For employees in other locations, benefits are tailored to local regulations and employment status to ensure competitive support worldwide.

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More about this job

Responsibilities

Own commercial analytics models, certified revenue and go-to-market metrics, and the governed semantic layer built primarily with dbt on Databricks. Improve data quality and AI readiness, deliver forecasting and other business-facing data products, and establish engineering standards while partnering with business leaders and data engineering.

Requirements

Requires 5+ years building production data models, including at least 3 years of dbt experience, strong SQL, Salesforce data-model knowledge, and fluency in SaaS revenue metrics. Candidates should also have experience owning business metric definitions, production data quality, Git-based engineering workflows, and communicating modeling decisions to non-technical stakeholders.

Skills

Data Modeling
dbt
SQL
Databricks
Salesforce
SaaS Revenue Metrics
Metric Definition
Data Quality Testing
Git
CI/CD
Unity Catalog
Semantic Layers
Data Governance
Feature Engineering
Stakeholder Communication
Mentoring

Benefits

Medical Insurance
Dental Insurance
Vision Insurance
Life Insurance
Short-Term Disability Coverage
Long-Term Disability Coverage
Retirement Match
Flexible Paid Time Off
Paid Parental Leave
Remote Work Stipend
Professional Development Reimbursement
Company Bonus Plan

Visa sponsorship

Not detected in the job text

Keywords

Analytics Engineering
dbt
Databricks
Delta
SQL
Salesforce
Unity Catalog
Metric Views
Semantic Layer
MetricFlow
Cube
Looker
LookML
MCP
Power BI
AI/BI Dashboards
ETL
Data Modeling
Data Contracts
Data Lineage
Data Quality
CI/CD
Git
ARR
MRR
Bookings
Revenue Retention
Feature Engineering
Forecasting

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