Sr. Analytics Engineer - Backblaze

About the Role

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 & Insights team operates in three layers: Data Engineering makes data reliable, Analytics Engineering makes data trustworthy and reusable, and Business Intelligence makes data useful.

This role sits in the middle layer. You will own the shared, reusable transformation and metric logic that everything else depends on. 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 duplicated and conflicting logic, and establish the engineering standards that make our data trustworthy at scale.

What You’ll Do

Own the Transformation Layer

• Design, build, and maintain dbt models from staging through marts, applying software engineering best practices: version control, code review, testing, CI/CD, and documentation.
• Establish and enforce modeling standards, naming conventions, and a tested, documented codebase as the function scales.
• Migrate business logic currently trapped in BI-tool calculated fields and manual SQL into governed, reusable, version-controlled models.

Build and Own the Semantic Layer

• 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.
• Eliminate duplicated and conflicting metric definitions across reports and tools.
• Partner with Finance, Revenue Operations, and Marketing to align on definitions, grain, and ownership before metrics are certified.

Deliver Certified Data Products

• Build a repeatable pipeline for shipping data products (land, stage, model, certify, expose).
• Deliver core data products such as Revenue, Customer Identity, and Funnel, each with clearly defined schema, grain, owners, tests, and lineage.
• Support financial-reporting-relevant revenue models with full auditability and reconciliation to Finance source numbers.

Drive Data Quality, Lineage, and Trust

• Implement testing, data quality checks, and observability so issues are caught before they reach stakeholders.
• Build human-readable lineage and documentation that make it clear how a number is produced from source to metric.
• Raise end-user confidence through discoverability, stewardship, and clear ownership.

Partner Cross-Functionally

• Translate business requests into defined entities, metrics, grain, and source of truth.
• Work closely with Data Engineering upstream, Business Intelligence downstream, and stakeholders across Finance, Revenue Operations, and Marketing.
• Build data models that are AI-ready for downstream automation and self-service analytics.

Right Fit

• 8+ years of experience in analytics engineering, data engineering, business intelligence, or a closely related role.
• Advanced SQL, with proven experience building production-grade, well-tested analytical data models.
• Strong hands-on dbt experience: modeling from staging to marts, tests, macros, documentation, and CI/CD.
• Hands-on experience building and running dbt on a modern cloud data warehouse; direct Snowflake strongly preferred.
• Experience building or operating a semantic/metric layer (dbt Semantic Layer/MetricFlow, Cube, LookML, or comparable).
• Experience applying software engineering principles to analytical work: Git, code review, testing, and CI/CD.
• Solid grounding in data modeling (dimensional and/or domain-driven) and in defining metrics from ambiguous requirements.
• Proactive self-starter who thrives in ambiguous, fast-moving environments.
• Excellent written and verbal communication skills in English.
• Able to maintain meaningful working-hours overlap with US Pacific time zone teams.