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, visualizations, dashboards, or other tools.

Preferred Qualifications

• Experience building data products or features directly used by consumer-facing products.

• Experience working with high-volume event, behavioral, sensor, geospatial, or time-series data.

• Familiarity with statistics, machine learning, or feature engineering.

• Experience taking a data problem from an ambiguous question through exploration, prototyping, evaluation, and production.

• Experience building internal tools or visualizations for exploring and debugging complex dataset