ФранцияLocation Data engineer
3 нед. назад
ФранцияMiddleOnsite
data pipelinesdata visualizationfeature engineering
Location Data Engineer role focusing on building data pipelines and transforming large-scale data for product use.
О компании
- 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 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.
Обязанности
- 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.
Требования
- 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.
Будет плюсом
- 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.
Другое
- 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.