Analytics Engineer IV, ACV Capital

ACV•Buffalo, NY

22d

About The Position

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.

Requirements

BA/BS in Statistics, Mathematics, Computer Science, Operations Research, or related
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
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

Master's or Ph.D. a plus, but offset by demonstrated experience and a deep toolbox
Experience with Google Cloud Platform
Familiarity with AI-assisted analytics or developer workflows
Exposure to credit risk, payment systems, or audit/compliance reporting contexts

Responsibilities

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
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
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
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