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Sr Analytics Engineer

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Salary

$140,000 - $154,000

Location

Remote. We are open to candidates in the following states: California, Colorado, Illinois, Massachusetts, Mississippi, Nebraska, New Jersey, New York (excluding Boroughs), North Carolina, Pennsylvania, Texas, Utah, and Washington.

Posted

Today

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.

What you'll need

  • 7+ years of experience in Analytics Engineering, Data Engineering, Business Intelligence Engineering, or a similarly technical data role
  • Advanced SQL and hands-on experience building production-scale transformations in a cloud data warehouse, ideally Snowflake, dbt, and GitHub or close equivalents
  • A strong understanding of dimensional modeling, data marts, data quality testing, documentation, version control, code review, and software development lifecycle practices
  • Experience with at least one data integration, ETL/ELT, orchestration, or event-data platform, plus a solid understanding of how data moves from source systems through transformation and into downstream reporting
  • Experience building semantic models, DAX measurements, dashboards, and self-service data products using Power BI or an equivalent modern BI platform
  • The ability and judgment to take a highly ambiguous business problem, ask the right questions, and turn it into something reliable and maintainable, plus the communication skills to do that with deeply technical partners and stakeholders who just need the data to make sense
  • Experience building semantic layers, AI agents, or automated workflows that support generative AI and natural-language analytics and a willingness to use AI responsibly to accelerate technical work, automate repeatable processes, and continuously expand your capabilities

Bonus points

  • Hands-on experience with Matillion or Segment
  • Experience supporting digital event instrumentation and transforming customer journey, product usage, and marketing data into trusted, analytics-ready datasets, especially in support of Marketing, Product, Finance, or executive decision-making
  • Familiarity with APIs, JSON, Python, Git, CI/CD, and automated deployment workflows
  • Experience with data observability, lineage, governance, privacy, role-based access, and warehouse cost optimization

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