Drimva

Oura

Senior Data Analyst, Hardware & Supply Chain

$148K–$203K

Hybrid - San Francisco, California +1 · Senior · Full-time · Posted Jul 2, 2026

Location
Hybrid - San Francisco, California
San Francisco, CA, United States
Experience
7+ years in analytics, business intelligence, or a related data-focused role, with at least 3+ years specifically supporting Hardware, Supply Chain, Operations, Manufacturing, or Logistics.

About the job

Leads analytics for Oura’s hardware and supply chain, building governed, audit-ready data products and reporting as the company migrates to Databricks.

What you'll do

  • Define and own hardware and supply chain KPIs, data products, lineage, controls, and audit documentation.
  • Migrate legacy reporting to Databricks and coordinate data ingestion and modeling across ERP, manufacturing, logistics, and e-commerce systems.
  • Partner with Operations and Supply Chain on planning, inventory, fulfillment, supplier analytics, and self-service reporting.

What they ask for

  • At least 7 years in analytics, business intelligence, or a related data-focused role, including 3+ years supporting hardware, supply chain, operations, manufacturing, or logistics.
  • Experience building executive reporting for a physical-product business that withstands external scrutiny.
  • Deep knowledge of hardware and supply chain data, planning data, and dimensional modeling.
  • Highly fluent in SQL; experience with dbt, including tests, documentation, exposures, and contracts.

Nice to have

  • Databricks experience; deep Snowflake experience is acceptable with willingness to migrate.
  • Experience with modern BI tools and working alongside external auditors.

Pay

USD $172,000- $203,000 base salary (Region 1); USD $158,950- $187,000 base salary (Region 2); USD $147,900- $174,000 base salary (Region 3)

Tools

SQL, dbt, Databricks, Unity Catalog, Databricks SQL, Genie, Snowflake, Tableau

AI focus

Evaluate AI-assisted SQL/modeling tools such as Cursor and Claude Code, and publish governed metrics for MCP/agentic AI consumption.

Model-written summary · the original posting is authoritative

First seen Oct 27, 2026 · Last verified Nov 5, 2026 (UTC)