Role Description 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. 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. Qualifications - 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. Requirements - 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. Benefits - Multiple medical plans including a high deductible, low cost health plan. - Company-sponsored (paid) Short-Term Disability, Long-Term Disability, and Life Insurance. - Comprehensive optional benefits such as Dental, Vision, Supplemental Life/AD&D, Legal/ID Protection, and Accident and Critical Illness Insurance. - Generous paid time off options, including uncapped vacation days, the greater of 3 paid sick days or in accordance with the applicable state or local paid sick leave law, 6 paid company holidays, 2 floating holidays, parental leave, bereavement leave, jury duty leave, voting leave, and other forms of paid leave as required by applicable law or regulation. - Employee Stock Purchase Program with additional opportunities to earn stock in the Company. - Retirement planning through the Company’s 401(k).

• Design, develop, and maintain ETL/ELT pipelines using Azure Data Factory and Microsoft Fabric Data Pipelines • Build scalable data ingestion frameworks for structured and semi-structured data (SQL, APIs, flat files, streaming sources) • Develop transformations using Synapse SQL, Spark (Synapse/Fabric), or Dataflows Gen2 • Optimize pipeline performance, reliability, and cost efficiency • Develop and manage solutions leveraging: Azure Synapse Analytics (dedicated & serverless pools) • Microsoft Fabric (Lakehouse, Warehouse, Data Engineering experiences) • Azure Data Lake Storage Gen2 • Implement medallion architecture (Bronze/Silver/Gold layers) where applicable • Collaborate with architects to design scalable data platform solutions • Design and implement MDM integration workflows with Reltio • Build and maintain pipelines to ingest, synchronize, and publish master data • Work with business and data governance teams on: Data quality rules • Matching and survivorship logic • Golden record management • Integrate Reltio with downstream analytics and operational systems • Implement data validation, cleansing, and lineage tracking • Ensure compliance with governance policies and standards • Support auditing, monitoring, and alerting for data pipelines • Work closely with data architects, analysts, and business stakeholders • Participate in Agile delivery (Scrum/Kanban) • Mentor junior developers • Contribute to coding standards and best practices • Participate in a rotating on-call support schedule for production data pipelines and platforms • Troubleshoot and resolve production issues, ensuring minimal disruption to business operations • Perform root cause analysis and implement long-term fixes for recurring issues

• Provides a critical role in meeting the Internal Audit and Compliance Integrity Services strategic initiative of maximizing the value of data analysis and visualization. • Combines an understanding of Sharp information systems and applications with the sophisticated use of data analysis and visualization technology to evaluate the effectiveness of controls, provide insights that contribute to the overall audit strategy, increase departmental productivity, and provide continuous auditing and monitoring of internal controls. • Collaborates with stakeholders in obtaining input on projects, developing and achieving consensus on the design and implementation of continuous auditing and monitoring projects. • Participates in audits, special projects, and other duties as assigned. • Designs and implements continuous auditing and monitoring programs to provide ongoing assurance over key business processes.

• Design and implement the end-to-end data transformation lifecycle using dbt Cloud and Databricks. • Build and evolve the diagnostic engine that detects Product Gaps, Operational Errors, and Technical Hotfixes. • Transform technical complexity into high-performance, self-describing Data Products. • Serve as the technical escalation point, define best practices for modeling, testing, and documentation. • Lead the transition toward a decentralized data model, developing self-serve standards and spearheading AI agent initiatives.