ironquill.tech/board

$ cat jobs/analytics-engineer-higharc-7fc7162ecc8e.json

Analytics Engineer

higharc·US·United States·mid
bigquery
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About Us Higharc is a VC-backed startup that is changing how new homes are designed and built. Join a founding team who’ve shipped products for Autodesk, Electronic Arts, Nike, and Apple. We have raised over $175M with support from top-notch venture capital firms and more than 18 strategic investors—industry leaders in construction, building products manufacturing, and distribution. Higharc is seeking an expert Analytics Engineer to own how data is structured, understood, and surfaced across our platform for the builders who use our product and for the Product and Customer Success teams who depend on their own internal analytics. This is a net-new role on the Software Engineering team, created because our largest customers are asking harder analytics questions than anyone currently owns. What You'll Do You own the data itself: the analytics models, the metric definitions, and the views built in tools like Looker and Observable. You decide what belongs in front of which audience, and you direct the work our infrastructure and engineering teams need to do to get the right data into the system in the first place. You'll turn current workflows into a coherent plan, then build against it. Expect to: Design, build, and maintain scalable data models and views in Looker/Observable that serve both customer-facing and internal analytics Partner with engineering to design and ship polished customer-facing data views for enterprise builders Work with Product and Customer Success to define the metrics they need to make decisions, then build the reporting behind them Set direction for our analytics stack and decide where each piece of reporting should live Own decisions about data infrastructure and instrumentation, including how data flows between systems like BigQuery and S3, and where product analytics tracking such as PostHog needs to change Use data modeling, statistics, and analytical techniques to identify trends, patterns, and anomalies across large datasets Build a triag

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