Location Data engineer

Amo·Parismid

In short

→Ingeniero de datos especializado en datos de ubicación, transformando datos brutos en señales útiles para productos.
→Día a día: construir pipelines, explorar datos complejos, crear features y herramientas de análisis para equipos de producto e ingeniería.
→Destacado: trabajo directo entre análisis exploratorio y sistemas en producción, con enfoque en datos de comportamiento y geolocalización.

Experiencia con datos de alta volumetría, comportamiento, sensores, geoespaciales o series

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What they ask for

✓Fundamentos sólidos en ingeniería de software y sistemas de datos intensivos.
✓Experiencia con SQL y manipulación de grandes conjuntos de datos.
✓Conocimiento de herramientas modernas como Spark, DBT, Dagster, BigQuery, ClickHouse o DuckDB.
✓Habilidades analíticas para explorar datos, probar hipótesis y entender patrones.
✓Capacidad para convertir análisis exploratorios en pipelines y features listos para producción.
✓Habilidad para comunicar hallazgos mediante métricas, visualizaciones y dashboards.

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SparkDBTDagsterBigQueryClickHouseDuckDBSQLPythonGitJupyter

Who should you write to at Amo?

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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. 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, mach

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