Data Engineer, Places

What You’ll Do Build pipelines for place and location-related data. Work with noisy real-world signals and large geospatial datasets. Improve quality, reliability, and observability of derived datasets. Define metrics and feedback loops for data quality and attribution. Prototype in Python, SQL, notebooks, or similar tools. Integrate scoped production experiments with amo’s backend systems. Run offline evaluations and A/B tests where relevant. Must Have Strong Python and SQL. Production experience with Python or Scala. Experience with PySpark, Spark SQL, Spark Streaming, Parquet, and Iceberg. Experience with large datasets and data pipelines. Experience with geospatial data, POI datasets, place matching, or similar real-world entity data. Comfort with metrics, experimentation, and data quality measurement. Comfort working near backend systems and reading production code. Nice To Have Experience in maps, mobility, local search, POI data, or geospatial systems. Experience with ranking, recommendations, embeddings, or entity resolution. Experience with Flink or similar stream processing systems. Experience reading or making scoped changes in Rust-backed systems. 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 Opera (Metro lines 3,8,9 and RER A). We have a beautiful Parisian-style