Fieldguide - Senior Analytics Engineer
Requirements
• 4+ years in a modern data stack, with advanced SQL and deep hands-on dbt expertise across modeling, testing, and project structure. • 4+ years • An engineer at heart: you ship production-quality Python, work fluently with version control and CI, and are comfortable diagnosing pipeline failures end to end. • A skilled dimensional modeler (Kimball, slowly changing dimensions) who designs data models built to last as the business evolves. • Analytically fluent, with a strong sense for data quality and the judgment to validate that results are correct, not just computed. • Deep experience with BigQuery and cloud data warehousing, including cost and performance optimization. • A collaborative partner who thrives working across teams, coordinating with Infrastructure and App Platform teams to deliver shared outcomes. • Based in SF and energized by in-person collaboration with our team. • A generalist background, with prior time as a data engineer, software engineer, or data scientist on top of the analytics-engineering core. • Experience feeding LLM or AI-native data interfaces (semantic layer, MCP, text-to-SQL guardrails). • BI migration experience (Looker, Omni, Tableau). • A regulated-domain background where correctness is non-negotiable.
Responsibilities
• Design and own the Insights semantic layer, establishing canonical definitions for the tables, columns, and metrics behind each report so the numbers we ship are trusted and consistent across the business. • Standardize how we define core metrics, reconcile them across data sources such as Amplitude, and clearly document any variances. • Migrate production report logic into governed, tested, and portable dbt models, strengthening test coverage and reusable macros along the way. • Improve the reliability of business-critical pipelines through proactive monitoring, alerting, and resilience to upstream schema changes. • Partner with customers and internal teams on data quality and new environment launches, including triage, onboarding, and data handling requirements. • Contribute to the design of a Kimball-style dimensional warehouse built to serve both traditional BI and emerging LLM and MCP-based analytics. • Help drive the migration of BI reporting onto the semantic layer, from report inventory and classification through cutover planning.
Benefits
• Competitive compensation packages with meaningful ownership • Wellness benefits starting on your first day • Technology and work-from-home reimbursement • Flexible work schedules
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