Location Data engineer

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We are seeking a highly skilled Location Data Engineer to join our team. In this role, you will work with large-scale datasets generated by our products and turn raw data into meaningful signals, insights, and features. You will work across the entire data lifecycle: building reliable pipelines, exploring and understanding complex datasets, developing features from them, and creating the tools and visualizations needed to understand their quality and impact. This is a technical and product-oriented data role.

You'll collaborate closely with product, engineering, and data teams to find what we can learn from our data and turn those learnings into production systems. As a Data Engineer, your day-to-day will include: Making Sense of Data Explore large and complex datasets to understand user behavior and identify useful patterns and signals. Transform raw data into reliable, well-defined features that can be used by our products and engineering teams.

Develop a deep understanding of our data: where it comes from, what it represents, its limitations, and how it can be combined to answer new questions. Building Data Products Design, build, and maintain pipelines that process large volumes of data efficiently and reliably. Take ideas from exploration to production: prototype them on historical data, evaluate their quality, and build the pipelines needed to run them at scale.

Build datasets and features that can power product experiences, internal systems, analytics, and machine learning models. Work with technologies such as Spark, DBT, Dagster, BigQuery, ClickHouse, DuckDB, or similar tools depending on the problem at hand. Exploring & Analyzing Use data to investigate hypotheses, understand behaviors, and answer ambiguous questions. Develop metrics and evaluation frameworks to understand whether the signals and features we build actually work.

Create analyses, dashboards, and visualizations that make complex datasets understandable and help the team make better decisions. Build tooling that makes it easier to inspect individual examples, debug data pipelines, and understand why a system produces a particular result. From Data to Intelligence Work closely with engineers and product teams to identify opportunities where data can make our products smarter.

Use statistical methods, heuristics, experimentation, or machine learning depending on what is most appropriate for the problem. Iterate on features and models based on real-world data and continuously improve t