Analytics Engineer III, Assurance
ACV Auctions
What you will do: The Analytics Engineer III on the ACV Assurance team is a skilled practitioner who transforms raw data into trusted, decision-ready models and reports that drive the business forward. Sitting at the intersection of data engineering and business intelligence, this role contributes across the full analytics stack - from dbt model design and data quality to Omni BI dashboards - and partners with the Assurance Business to surface insights on arbitration risk and product initiatives.
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 business stakeholders can answer common questions themselves.
Analytics Modeling & Data Quality
Design, build, and maintain dbt models (staging, intermediate, production layers) that serve as the single source of truth for Assurance 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 Assurance stakeholders and keep definitions governed in our semantic layer
Build upon our 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
Assurance Business Domains
Risk:
support the business in developing data, predictive, and forecasting models that identify high-risk dealer and vehicle populations to support program management strategy
Product:
develop dealer-facing dashboards that illustrate their arbitration trends over time
Project Ownership & Stakeholder Partnership
Execute scoped projects with general guidance: define approach and timelines with input from a manager or senior team member, and drive day-to-day execution independently
Deliver data-driven recommendations
Navigate competing priorities across multiple stakeholder groups; propose win-win solutions when technical requirements conflict
What you will need: 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
3+ years of professional experience in analytics engineering, data engineering, or BI
Hands-on production experience with dbt (model design, testing, documentation, incremental strategies)
Expert-level SQL; comfortable with window functions, complex joins, and query optimization in BigQuery or a comparable cloud warehouse
Experience delivering analytical initiatives independently, from scoping through stakeholder presentation
Experience with Git-based version control workflows
Soft Skills
Excellent communicator with a strong attention to detail; genuinely curious about the business and willing to dig into the specifics to get things right
Collaborative, low-ego, and invested in the team's collective output
Strong instinct for knowing when to answer quickly vs. when to build properly
Communicates analytical findings clearly to non-technical audiences
Comfortable navigating ambiguity
Nice-to-Haves
Proficiency building semantic layers; Omni or Looker BI experience preferred, but similar experience considered
Experience with Google Cloud Platform
Familiarity with AI-assisted analytics or developer workflows
Experience with binary classification and/or loss forecasting
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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 business stakeholders can answer common questions themselves.
Analytics Modeling & Data Quality
Design, build, and maintain dbt models (staging, intermediate, production layers) that serve as the single source of truth for Assurance 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 Assurance stakeholders and keep definitions governed in our semantic layer
Build upon our 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
Assurance Business Domains
Risk:
support the business in developing data, predictive, and forecasting models that identify high-risk dealer and vehicle populations to support program management strategy
Product:
develop dealer-facing dashboards that illustrate their arbitration trends over time
Project Ownership & Stakeholder Partnership
Execute scoped projects with general guidance: define approach and timelines with input from a manager or senior team member, and drive day-to-day execution independently
Deliver data-driven recommendations
Navigate competing priorities across multiple stakeholder groups; propose win-win solutions when technical requirements conflict
What you will need: 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
3+ years of professional experience in analytics engineering, data engineering, or BI
Hands-on production experience with dbt (model design, testing, documentation, incremental strategies)
Expert-level SQL; comfortable with window functions, complex joins, and query optimization in BigQuery or a comparable cloud warehouse
Experience delivering analytical initiatives independently, from scoping through stakeholder presentation
Experience with Git-based version control workflows
Soft Skills
Excellent communicator with a strong attention to detail; genuinely curious about the business and willing to dig into the specifics to get things right
Collaborative, low-ego, and invested in the team's collective output
Strong instinct for knowing when to answer quickly vs. when to build properly
Communicates analytical findings clearly to non-technical audiences
Comfortable navigating ambiguity
Nice-to-Haves
Proficiency building semantic layers; Omni or Looker BI experience preferred, but similar experience considered
Experience with Google Cloud Platform
Familiarity with AI-assisted analytics or developer workflows
Experience with binary classification and/or loss forecasting
#LI-AM3
#J-18808-Ljbffr
Reference: WJ-3875_13397499