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 datasets.
  
  

Life at amo

To ensure that everyone is set up for success within our way of working, we work together onsite 5 days a week.

We wanted to make sure coming to the office was as comfortable as possible for you:

We chose a location in central Paris, near Opéra (Metro lines 3, 8, 9 and RER A).

We have a beautiful Parisian-style office with high ceilings, balconies, and huge windows. So a lot of natural light!

Because life outside of work should also be stress-free, we cover:

• Health care (100% coverage).

• Maternity Leave, Paternity Leave, Second Parent Leave (salary maintained at 100%).

• Vacation days: 8–9 weeks (total) per year:

• 5 weeks of paid time off;

• 1–2 additional weeks (RTT, based on our company contract);

• ~11 bank holidays.

We shut down entirely twice a year, two weeks in summer and one week in winter, to allow everyone to truly recharge and avoid prolonged slowdowns. These pauses are part of your total vacation time, giving everyone a real chance to unplug and recharge. No Slack, no email, no FOMO.

We love the diverse perspectives we get from having people from all over the world join us (68% of our team is international), and so we of course support relocation to Paris with:

• Help and sponsored visa process.

• 1 month of Airbnb 100% covered by amo upon arrival.

• Assistance from a trusted relocation agency to find your permanent home.

• Help with French paperwork like opening a French social security account, tax forms, getting your carte vitale (free healthcare), and more.

• French lessons to be fully set with your new Parisian life.