Sr. Analytics Engineer

About this role

<p><strong>About the Role</strong></p> <p>Backblaze is building its data function into a governed, product-oriented capability that delivers trusted, auditable, and actionable data across the company. The Data Platform &amp; Insights team operates in three layers: Data Engineering makes data reliable (ingestion, pipelines, Snowflake architecture, raw and staging layers), Analytics Engineering makes data trustworthy and reusable (dbt models, the semantic layer, certified metric definitions, lineage, and the data products themselves), and Business Intelligence makes data useful (dashboards, ad hoc analysis, and stakeholder reporting).</p> <p>This role sits in the middle layer. You will own the shared, reusable transformation and metric logic that everything else depends on. This is not a pipeline or ingestion role, and it is not a dashboard-building role. It is the role that turns landed source data into governed, certified, single-source-of-truth models that the rest of the business consumes.</p> <p>As an early member of the Analytics Engineering function, you will help define how data products are built and shipped at Backblaze. You will design the dbt models and semantic layer that produce our certified business and financial metrics, eliminate the duplicated and conflicting logic that lives across our reporting tools today, and establish the engineering standards that make our data trustworthy at scale. Because Backblaze is a public company, the models you build directly support financial reporting, so accuracy, lineage, and reconciliation with Finance are core to the work.</p> <p>This is a high-autonomy, high-leverage role for someone who wants to build a modern analytics foundation from the ground up, not maintain a finished one.</p> <p><strong>What You’ll Do</strong></p> <p><em>Own the transformation layer</em></p> <ul> <li>Design, build, and maintain dbt models from staging through marts, applying software engineering best practices: version control, code review, testing, CI/CD, and documentation</li> <li>Establish and enforce modeling standards, naming conventions, and a tested, documented codebase as the function scales</li> <li>Migrate business logic currently trapped in BI-tool calculated fields and manual SQL into governed, reusable, version-controlled models</li> </ul> <p><em>Build and own the semantic layer</em></p> <ul> <li>Stand up Backblaze’s semantic layer and define certified, single-source-of-truth metrics such as ARR, MRR, paying customer count, retention, and GTM and funnel metrics</li> <li>Eliminate duplicated and conflicting metric definitions across reports and tools so that one metric means one thing everywhere</li> <li>Partner with Finance, Revenue Operations, and Marketing to align on definitions, grain, and ownership before metrics are certified</li> </ul> <p><em>Deliver certified data products</em></p> <ul> <li>Build a repeatable pipeline for shipping data products (land, stage, model, certify, expose) so each new product compresses delivery time rather than starting from scratch</li> <li>Deliver core data products such as Revenue, Customer Identity, and Funnel, each with clearly defined schema, grain, owners, tests, and lineage</li> <li>Support financial-reporting-relevant revenue models with full auditability and reconciliation to Finance source numbers</li> </ul> <p><em>Drive data quality, lineage, and trust</em></p> <ul> <li>Implement testing, data quality checks, and observability so issues are caught before they reach stakeholders</li> <li>Build human-readable lineage and documentation that make it clear how a number is produced from source to metric</li> <li>Raise end-user confidence through discoverability, stewardship, and clear ownership</li> </ul> <p><em>Partner cross-functionally</em></p> <ul> <li>Translate “build me a report” into defined entities, metrics, grain, and source of truth</li> <li>Work closely with Data Engineering (your upstream) and Business Intelligence (your downstream), and with business stakeholders across Finance, Revenue Operations, and Marketing</li> <li>Build data models that are AI-ready, so that downstream automation and self-service analytics inherit the benefit of a trusted foundation</li> </ul> <p><strong>Right Fit</strong></p> <ul> <li>8+ years of experience in analytics engineering, data engineering, business intelligence, or a closely related role</li> <li>Advanced SQL, with proven experience building production-grade, well-tested analytical data models</li> <li>Strong hands-on dbt experience: modeling from staging to marts, tests, macros, documentation, and CI/CD</li> <li>Hands-on experience building and running dbt on a modern cloud data warehouse; direct Snowflake strongly preferred</li> <li>Experience building or operating a semantic / metric layer (dbt Semantic Layer / MetricFlow, Cube, LookML, or a comparable framework)</li> <li>Experience applying software engineering principles to analytical work: Git-based version control, code review, testing, and CI/CD</li> <li>Solid grounding in data modeling (dimensional and/or domain-driven) and in defining metrics and business logic from ambiguous requirements</li> <li>A proactive self-starter who thrives in ambiguous, fast-moving environments and drives projects end to end, from definition through stakeholder onboarding</li> <li>Excellent written and verbal communication in English, with a track record of working effectively with both technical and non-technical stakeholders</li> <li>Able to maintain meaningful working-hours overlap with US Pacific time zone teams</li> </ul> <p><strong>Bonus Points</strong></p> <ul> <li>Experience supporting financial reporting, public-company metrics, or SOX-compliant data in a regulated environment</li> <li>Experience with GTM, revenue, and subscription/billing data and systems (Salesforce, Stripe, NetSuite, Orb, or similar)</li> <li>Experience with Tableau or Tableau Next as a consumption layer</li> <li>Proficiency in Python for au