Analytics Engineer

Half product manager, half data engineer. You'll define KPIs, shape priorities, and help the business understand what the data is actually saying, all while staying technical enough to write the queries, build the pipelines, and eventually get hands-on with the models.

Team: Data & Analytics
Location: Remote-friendly · Buffalo/WNY hybrid option available
Employment: Full-time
Experience: 4+ years in analytics or data engineering

SelectFI builds AI-powered tools for the automotive finance space, helping dealerships and lenders make smarter, faster credit decisions through predictive modeling and intelligent workflows.

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.

You'll own the reporting and insight layer that customer success, sales, and product rely on. You'll write the queries, build the dashboards, and monitor the feedback signals that keep our data science team calibrated. Over time you'll move deeper into the data science work: validating model performance, shaping improvement cycles, and helping automate what today requires too much manual effort.

• Monitor behavioral and explicit feedback signals (prediction bypasses, thumbs-down events, model acceptance rates) to surface performance issues before they become customer problems.

• Triage inbound requests from all feedback signals, scope the analysis, and translate findings into prioritized items for the data science team, eventually assisting hands-on.

• Build and improve automated feedback mechanisms that capture signal at scale, reducing reliance on manual escalations and making pattern detection faster.

• Quantify the lift potential of identified model issues and help the team prioritize which investigations are worth the investment.

• Build custom reports and dashboards for salespeople, managers, and leadership, surfacing ROI, usage, and performance metrics.

• Write reusable, well-documented Redshift queries that engineering can build product features on top of.

• Design and ship a report card system: personalized, automated performance summaries for salespeople.

• Build structured onboarding data checks that verify lender configurations, integrations, and training prerequisites before a dealer goes live.

• Define and track business KPIs for leadership. Handle ad hoc data requests for the broader team.

• Help the Customer Success team understand what the dashboards show, where the figures come from, and what the metrics mean.

• Educate CS on metric definitions and data sources so they can answer customer questions without escalating.

• Develop subject matter expertise in automotive lending and help build a shared understanding of the problem domain across the company.

• Build and maintain pipelines that ingest semi-structured data (JSON, CSV, PDFs), transform it, and make it analytics- and ML-ready.

• Collaborate with the data science team to validate and tune predictive models against real lender outcomes.

• Build AI-assisted workflows that automate routine analytical tasks as the tooling and your domain knowledge develop.

Required

• 4+ years of experience in an analytics engineer, BI engineer, or data analyst role.

• Strong SQL skills: complex queries, optimization, validation, and writing reusable query libraries.

• 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 transforming semi-structured data into analytics-ready formats.

• Clear communicator who can translate ambiguous feedback and behavioral signals into scoped, 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 Have

• Fintech 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, code assistance. Claude Code is heavily used here.

• Familiarity with React or JavaScript-based tooling; helpful context when scoping data requirements for front-end features.

30 days: You know the Redshift schema, have shipped your first ad hoc report, met the CS team, and understand the feedback signals we use to track model performance and customer health.

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 what automated signals to push to dealers: usage summaries, report cards, savings, and opportunities.

90 days: Fully autonomous on the reporting layer, first model signal analysis complete. Feeding the data science team a steady stream of tactical improvement tickets grounded in real signal. Clear POV on what to build next.

$120,000–$145,000 base salary, depending on experience. Benefits include health, dental, and vision insurance, and unlimited PTO.

Ownership. You'll own the insight and feedback layer from the ground up: the queries that power our dashboards, the signals that drive our ML roadmap, and the systems that make both scale.

Growth. A direct path into data science and model work as your domain expertise deepens and the team grows.

Environment. Small team, fast feedback, direct access to leadership. Remote-friendly with a hybrid option in Buffalo/WNY. Equity participation included.