Analytics Engineer IV, ACV Capital

  Who are we looking for:   The Senior Analytics Engineer on the ACV Capital team is an expert practitioner who transforms raw data into trusted, decision-ready models and reports that drive the lending business forward. Sitting at the intersection of data engineering and business intelligence, this role owns the full analytics stack - from dbt model design and data quality to Omni BI dashboards - and partners directly with Capital leadership to surface insights on lead targeting, loan origination, account management, dealer servicing, and operational compliance. A key objective of this role is reducing ad-hoc analytical bottlenecks over time. You will be expected to answer urgent business questions quickly and directly, while systematically building the underlying dbt models, metric definitions, and BI layer in a way that enables self-serve analytics - including AI-assisted querying - so that Capital stakeholders can answer common questions themselves.   What you will do (Responsibilities): Analytics Modeling & Data Quality Design, build, and maintain dbt models (staging, intermediate, production layers) that serve as the single source of truth for Capital KPIs, with machine-readability in mind Enforce data quality through dbt tests, source freshness checks, and documentation so downstream consumers can trust what they see Write complex SQL transformations on large datasets; optimize for cost and performance Reporting & BI Translate business questions into well-scoped analytical requirements; define metrics in collaboration with Capital leadership and keep definitions governed in our semantic layer Build and own the Omni BI semantic layer, enabling self-serve chat and dealer-facing embedded reporting Balance responsiveness to ad-hoc requests while optimizing via building: triage what should be answered once vs. what should be codified so stakeholders or AI tools can self-serve it in the future Deliver clear, compelling data narratives to non-technical stakeholders; support follow-on questions and iterate quickly Capital Business Domains Lead Targeting: develop models and dashboards that identify high-propensity dealer and borrower segments to support outbound sales strategy Loan Origination Tracking: build funnel visibility from application through funding; surface bottlenecks and conversion opportunities Operational Functions: provide analytical support for account management workflows, dealer servicing SLAs, and audit/compliance reporting Project Ownership & Stakeholder Partnership Own analytical initiatives end-to-end: identify stakeholders, define scope and timelines, and execute without requiring close supervision Proactively surface opportunities and deliver data-driven recommendations — not just answers to questions that were asked Navigate competing priorities across multiple stakeholder groups; propose win-win solutions when technical requirements conflict What you will need (Skills, Experience, Education): Education BA/BS in Statistics, Mathematics, Computer Science, Operations Research, or related Master's or Ph.D. a plus, but offset by demonstrated experience and a deep toolbox Experience 5+ years of professional experience in analytics engineering, data engineering, or BI Hands-on production experience with dbt (model design, testing, documentation, incremental strategies) Proficiency building semantic layers; Omni or Looker BI experience preferred, but similar experience considered Expert-level SQL; comfortable with window functions, complex joins, and query optimization in BigQuery or a comparable cloud warehouse Experience delivering major analytical initiatives independently, from scoping through stakeholder presentation Background in financial services, fintech, or lending is a meaningful plus - familiarity with origination, account management, or B2B lending workflows accelerates ramp Experience with Git-based version control workflows Soft Skills Communicates analytical findings clearly to non-technical audiences Comfortable navigating ambiguity Collaborative, low-ego, and invested in the team's collective output Strong instinct for knowing when to answer quickly vs. when to build properly Nice-to-Haves Experience with Google Cloud Platform Familiarity with AI-assisted analytics or developer workflows Exposure to credit risk, payment systems, or audit/compliance reporting contexts