Location Data engineer

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.

Your Skills & Experience

Strong software engineering fundamentals and experience working with data-intensive systems.

• Very comfortable with SQL and manipulating large datasets.

• Experience with one or more modern data technologies such as Spark, DBT, Dagster, BigQuery, ClickHouse, DuckDB, or equivalent tools.

• Strong analytical skills: you enjoy digging into data, testing hypotheses, and understanding why something behaves the way it does.

• Ability to turn exploratory analysis into reliable, production-ready data pipelines and features.

• Familiarity with data modeling, pipeline orchestration, and large-scale data processing.

• Ability to communicate findings clearly through metrics, v