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 Role We’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. What You’ll Do Design and own the analytics semantic layer , establishing canonical definitions for the tables, columns, and metrics behind customer-facing reports Standardize definitions for core metrics and reconcile data across multiple sources, clearly documenting discrepancies and variances Migrate production reporting logic into governed, tested, and portable dbt models Strengthen dbt test coverage and develop reusable macros and modeling patterns 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, environment launches, onboarding, triage, and data-handling requirements Contribute to the design of a Kimball-style dimensional data warehouse capable of supporting traditional BI as well as LLM- and MCP-based analytics Help drive the migration of existing BI reporting onto the semantic layer, including report inventory, classification, validation, and cutover planning Ensure analytics outputs are accurate, traceable, and capable of supporting enterprise-grade decision-making Who You Are 4+ years of experience working with a modern data stack Advanced SQL skills and deep hands-on experience with dbt , including modeling, testing, macros, and project architecture An engineer at heart who writes production-quality Python and works comfortably with version control and CI/CD Experienced diagnosing data and pipeline failures end-to-end Strong understanding of dimensional data modeling , including Kimball methodologies and slowly changing dimensions Highly analytical, with strong instincts around data quality and validating whether results are actually correct—not simply whether a query ran successfully Deep experience with BigQuery and cloud data warehousing , including performance and cost optimization Comfortable designing data models intended to evolve alongside a growing product and business Strong cross-functional collaborator who can work effectively with Engineering, Product, Infrastructure, and Platform teams Comfortable operating in a fast-moving environment where ownership and technical judgment are highly valued Bonus Points A generalist technical background that includes experience in data engineering, software engineering, or data science Experience building data infrastructure that supports LLMs or AI-native analytics interfaces Familiarity with semantic layers, MCP, text-to-SQL, or LLM guardrails Experience migrating BI environments such as Looker, Omni, or Tableau Experience working in regulated or mission-critical environments where data correctness is essential Experience building analytics systems for complex enterprise workflows What Should Excite You Owning the data foundation: Your work directly determines whether customer-facing analytics can be trusted Modern analytics engineering: Building governed semantic layers rather than maintaining disconnected reporting logic AI-native analytics: Creating data infrastructure that can support LLMs, MCP, and emerging ways of interacting with enterprise data Complex data modeling: Translating evolving business concepts into durable, scalable data models High standards for correctness: Working in an environment where data quality and defensibility genuinely matter Cross-functional impact: Partnering across Product and Engineering to influence how analytics capabilities evolve Benefits & Compensation Meaningful equity ownership Flexible PTO 401(k) Wellness benefits starting on your first day Technology and work-from-home reimbursement #J-18808-Ljbffr Empathy Talent