Senior Analytics Engineer

We’re building technology that helps modernize how assurance and audit professionals work across cybersecurity, privacy, and financial audits. The platform brings greater efficiency, visibility, and reliability to complex workflows that businesses depend on to establish trust.As part of a growing technology organization, you’ll have the opportunity to help shape both the product and the underlying data infrastructure while working alongside a collaborative, driven, and highly technical team.About the RoleWe’re hiring a Senior Analytics Engineer to own the data foundation behind a customer-facing analytics and insights platform.You’ll be responsible for the semantic layer, data pipelines, and models that make every metric and report reliable, consistent, and defensible. This is a highly technical role for an engineer who specializes in analytics and cares deeply about data quality.You’ll define the canonical tables, columns, and metrics behind customer-facing reporting, migrate production logic into governed dbt models, and strengthen the reliability of critical data pipelines. Your work will also serve as the foundation for API-driven and emerging LLM/MCP-based analytics, making correctness and scalability essential.You’ll partner closely with Product, Engineering, Application Platform, and Infrastructure teams while helping shape how sophisticated enterprise customers consume and interact with analytics.Location: San Francisco, CAWhat You'll DoDesign and own the analytics semantic layer, establishing canonical definitions for the tables, columns, and metrics behind customer-facing reportsStandardize definitions for core metrics and reconcile data across multiple sources, clearly documenting discrepancies and variancesMigrate production reporting logic into governed, tested, and portable dbt modelsStrengthen dbt test coverage and develop reusable macros and modeling patternsImprove the reliability of business-critical pipelines through proactive monitoring, alerting, and resilience to upstream schema changesPartner with customers and internal teams on data quality, environment launches, onboarding, triage, and data-handling requirementsContribute to the design of a Kimball-style dimensional data warehouse capable of supporting traditional BI as well as LLM- and MCP-based analyticsHelp drive the migration of existing BI reporting onto the semantic layer, including report inventory, classification, validation, and cutover planningEnsure analytics outputs are accurate, traceable, and capable of supporting enterprise-grade decision-makingWho You Are4+ years of experience working with a modern data stackAdvanced SQL skills and deep hands-on experience with dbt, including modeling, testing, macros, and project architectureAn engineer at heart who writes production-quality Python and works comfortably with version control and CI/CDExperienced diagnosing data and pipeline failures end-to-endStrong understanding of dimensional data modeling, including Kimball methodologies and slowly changing dimensionsHighly analytical, with strong instincts around data quality and validating whether results are actually correct—not simply whether a query ran successfullyDeep experience with BigQuery and cloud data warehousing, including performance and cost optimizationComfortable designing data models intended to evolve alongside a growing product and businessStrong cross-functional collaborator who can work effectively with Engineering, Product, Infrastructure, and Platform teamsComfortable operating in a fast-moving environment where ownership and technical judgment are highly valuedBonus PointsA generalist technical background that includes experience in data engineering, software engineering, or data scienceExperience building data infrastructure that supports LLMs or AI-native analytics interfacesFamiliarity with semantic layers, MCP, text-to-SQL, or LLM guardrailsExperience migrating BI environments such as Looker, Omni, or TableauExperience working in regulated or mission-critical environments where data correctness is essentialExperience building analytics systems for complex enterprise workflowsWhat Should Excite YouOwning the data foundation: Your work directly determines whether customer-facing analytics can be trustedModern analytics engineering: Building governed semantic layers rather than maintaining disconnected reporting logicAI-native analytics: Creating data infrastructure that can support LLMs, MCP, and emerging ways of interacting with enterprise dataComplex data modeling: Translating evolving business concepts into durable, scalable data modelsHigh standards for correctness: Working in an environment where data quality and defensibility genuinely matterCross-functional impact: Partnering across Product and Engineering to influence how analytics capabilities evolveBenefits & CompensationBase Salary: $175,000–$200,000Meaningful equity ownershipFlexible PTO401(k)Wellness benefits starting on your first dayTechnology and work-from-home reimbursementFlexible work schedules