Data Analytics Engineer

What you’ll doYou’ll own the reporting and insight layer that customer success, sales, and product rely on —writing the queries, building the dashboards, and monitoring the feedback signals that keep ourdata science team calibrated. Over time you’ll move deeper into the data science work:validating model performance, shaping improvement cycles, and helping automate what todayrequires too much manual effort.Feedback signals & model calibrationMonitor behavioral and explicit feedback signals - prediction bypasses, thumbs-down events,model acceptance rates - to surface performance issues before they become customerproblems.Triage inbound requests from all feedback signals, scope the analysis, and translate findingsinto prioritized items for the data science team, eventually assisting hands-on.Build and improve automated feedback mechanisms that capture signal at scale, reducingreliance on manual escalations and making pattern detection faster.Quantify the lift potential of identified model issues and help the team prioritize whichinvestigations are worth the investment.Reporting & business insightsBuild custom reports and dashboards for salespeople, managers, and leadership, surfacingROI, usage, and performance metrics.Write reusable, well-documented Redshift queries that engineering can build product featureson top of.Design and ship a report card system: personalized, automated performance summaries forsalespeople.Build structured onboarding data checks that verify lender configurations, integrations, andtraining prerequisites before a dealer goes live.Define and track business KPIs for leadership. Handle ad hoc data requests for the broaderteam.Customer & team enablementHelp the Customer Success team understand what the dashboards show, where the figurescome from, and what the metrics mean.Educate CS on metric definitions and data sources so they can answer customer questionswithout escalating.Develop subject matter expertise in automotive lending and help build a sharedunderstanding of the problem domain across the company.Data pipelines & ML supportBuild and maintain pipelines that ingest semi-structured data (JSON, CSV, PDFs), transformit, and make it analytics- and ML-ready.Collaborate with the data science team to validate and tune predictive models against reallender outcomes.Build AI-assisted workflows that automate routine analytical tasks as the tooling and yourdomain knowledge develop.Who we’re looking forRequired4+ years of experience in an analytics engineer, BI engineer, or data analyst role.Strong SQL skills: complex queries, optimization, validation, and writing reusable querylibraries.Python proficiency for data manipulation, scripting, and pipeline development.Hands-on AWS experience. Redshift required; QuickSight familiarity a plus.Solid data engineering fundamentals: ETL/ELT, data modeling, and transformingsemi-structured data into analytics-ready formats.Clear communicator who can translate ambiguous feedback and behavioral signals intoscoped, prioritized analytical work.Git and version control fluency, SDLC best practices, and a Bachelor’s or Master’s in CS,Engineering, Statistics, or a related field.Nice to haveFintech or financial data experience (lending, banking, or auto finance).Experience owning feedback loops, signal monitoring, or model evaluation workflows.AWS SageMaker and Glue.Exposure to ML model development or validation workflows.Comfort using AI/LLM tools for data work — query generation, anomaly detection, codeassistance. Claude Code is heavily used here.Familiarity with React or JavaScript-based tooling — helpful context when scoping datarequirements for front-end features.Your first 90 days30 days: You know the Redshift schema, have shipped your first ad hoc report, met the CSteam, and understand the feedback signals we use to track model performance and customerhealth.60 days: You've formed clear opinions on which dashboards each role needs — salesperson,manager, owner — and which to reconsider. You're partnering with CS to identify whatautomated signals to push to dealers: usage summaries, report cards, savings, andopportunities.90 days: Fully autonomous on the reporting layer, first model signal analysis complete. Feedingthe data science team a steady stream of tactical improvement tickets grounded in real signal.Clear POV on what to build next.Compensation & Benefits$120,000–$145,000 base salary, depending on experience. Benefits include health, dental, andvision insurance, and unlimited PTO.How to applyEmail a résumé and a short note on something you’ve built that you’re proud of — adashboard, a pipeline, a feedback or signal monitoring system, or a hairy SQL refactor.We read every application and reply within five business days.careers@selectfi.comAbout SelectFISelectFI builds AI-powered tools for the automotive finance space, helping dealerships andlenders make smarter, faster credit decisions through predictive modeling and intelligentworkflows.The lenders have always had the data. SelectFI gives that same advantage to dealers —predicting which lender will approve a deal, and how it should be structured, before submission.That moves F&I teams from guesswork to precision: deals are pencilled right the first time,approvals come faster, credit-pull costs go down, and lenders see cleaner submissions.