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LinkedInPosted 3 weeks ago

Tech Lead, Data Foundations

$138,000–$225,000 year

HybridSunnyvale, California, United States

Full TimeSenior LevelBachelors DegreeEnterpriseInternet

Job Summary

Architect and scale core data foundations enabling trusted Finance reporting, automation, and operational decisioning. Define the data operating model, build durable semantic and master data layers, and embed quality, lineage, and controls end-to-end. Partner with Finance Technology and Engineering to develop curated datasets, standardize metric definitions, and drive master data rigor across cost centers and chart of accounts. Lead cross-functional execution to manage dependencies and set measurable success metrics, ensuring workflows and RPA are powered by governed data assets. Elevate decision-making by turning data foundations into executive-ready insights through clear narrative and adoption enablement. This senior individual contributor role combines deep data architecture expertise with cross-functional leadership to build scalable, auditable platforms.

Required Qualifications

  • Bachelor's degree in a quantitative or technical field (or equivalent practical experience)
  • 6+ years in data foundations, analytics engineering, data governance, or related roles supporting business-critical stakeholders
  • 6+ years with advanced SQL and designing scalable data models and semantic layers
  • Demonstrated experience with data quality, lineage, documentation, and controls in production environments
  • Proven ability to align stakeholders across Finance, Engineering, and Finance Technology and deliver durable platforms

Desired Qualifications

  • Finance domain fluency: Experience building finance data platforms (GL, planning, close, cost centers, revenue, headcount)
  • Experience with MDM domains and workflows; ERP/EPM familiarity (Oracle, SAP, etc.)
  • Experience with modern data stack tools (e.g., warehouse/lakehouse, transformation frameworks, catalog/lineage, CI/CD for data)
  • Experience providing governed data assets for automation and RPA programs
  • Ability to translate platform strategy into clear decisions, tradeoffs, and adoption plans

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