Location Data engineer

About the role

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 their accuracy and reliability. - Help bridge the gap between exploratory data work and robust systems running in production.

- Continuous Improvement - Improve the performance, reliability, and maintainability of our data infrastructure. - Monitor data quality and proactively investigate unexpected changes or anomalies. - Stay up to date with developments in data engineering, analytics, and machine learning, and bring relevant ideas and technologies into our stack. - Contribute to a culture of curiosity, craftsmanship, and learning.