Sr Analytics Engineer

ABOUT THIS POSITION Aceable is looking for a Sr Analytics Engineer who likes solving holistic data problems. This is a hands-on role that sits on a small team at the intersection of Data Engineering and Business Intelligence. In this role, you’ll be empowered to solve high-visibility data architecture and pipeline problems from beginning to end. One week you might build a new data pipeline or troubleshoot why data isn't landing where it should. The next week, you could be designing a dbt model, creating a semantic view or AI agent in Snowflake, building a Power BI dashboard, or partnering with a stakeholder to turn a fuzzy business question into a data product people can actually trust and use. We don't expect you to be an expert in every tool we use. We do expect meaningful technical depth in multiple areas, strong problem-solving instincts, experience and excitement around AI, and the curiosity to jump into unfamiliar territory when the problem calls for it. If you like variety, autonomy, solving messy problems, and building things from source data all the way through to the person making the decision, you will love working with us. WHAT YOU’LL DO - Own data products end to end.

Take a problem from source data all the way through to the dashboard, metric, or workflow someone actually makes a decision with. Design, build, test, deploy, and maintain the whole path rather than handing it off at each layer. - Turn fuzzy questions into things people trust. Work directly with partners in Marketing, Product, Finance, and Engineering to figure out what they're really asking, then build something reliable and maintainable enough that they stop asking you and just use it. - Own quality from source to screen. Build the testing, documentation, monitoring, and data quality controls that keep trust high, and when something breaks, chase it wherever it actually lives: pipeline, transformation, model, metric, or report. - Leave the platform better than you found it. Improve architecture, simplify workflows, and automate repetitive work, including using AI well to move faster on coding, investigation, testing, and documentation, while holding the line on accuracy and human review.