Note: The job is a remote job and is open to candidates in USA. Backblaze is building a governed, product-oriented data function that delivers trusted, auditable, and actionable data across the company. The Senior Analytics Engineer will own reusable dbt transformation models, the semantic layer, certified metrics, data products, testing, lineage, and cross-functional data standards supporting business and financial reporting.
Responsibilities
- 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
- 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 so that one metric means one thing everywhere
- Partner with Finance, Revenue Operations, and Marketing to align on definitions, grain, and ownership before metrics are certified
- 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
- 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
- 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
- Translate “build me a report” into defined entities, metrics, grain, and source of truth
- Work closely with Data Engineering (your upstream) and Business Intelligence (your downstream), and with business stakeholders across Finance, Revenue Operations, and Marketing
- Build data models that are AI-ready, so that downstream automation and self-service analytics inherit the benefit of a trusted foundation
Skills
- 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
- Experience building or operating a semantic / metric layer (dbt Semantic Layer / MetricFlow, Cube, LookML, or a comparable framework)
- Experience applying software engineering principles to analytical work: Git-based version control, code review, testing, and CI/CD
- Solid grounding in data modeling (dimensional and/or domain-driven) and in defining metrics and business logic from ambiguous requirements
- A proactive self-starter who thrives in ambiguous, fast-moving environments and drives projects end to end, from definition through stakeholder onboarding
- Excellent written and verbal communication in English, with a track record of working effectively with both technical and non-technical stakeholders
- Able to maintain meaningful working-hours overlap with US Pacific time zone teams
- Experience supporting financial reporting, public-company metrics, or SOX-compliant data in a regulated environment
- Experience with GTM, revenue, and subscription/billing data and systems (Salesforce, Stripe, NetSuite, Orb, or similar)
- Experience with Tableau or Tableau Next as a consumption layer
- Proficiency in Python for automation and workflow development
- Familiarity with pipeline orchestration (e.g., Airflow, Dagster) and ingestion tooling (e.g., Fivetran)
- Fluency with modern AI tools and coding assistants, applied to accelerate analytical and modeling work
- Hands-on experience building and running dbt on a modern cloud data warehouse; direct Snowflake strongly preferred
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