Location Data engineer

About this 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.

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.