Launchmetrics is the leading Brand Performance Cloud used by Fashion, Lifestyle and Beauty (FLB) executives to make smarter decisions around their branding efforts. Founded in NYC and headquartered in Paris, we have employees in ten markets worldwide and offer support in five languages.
We are a team of passionate engineers distributed across the world, building state‑of‑the‑art modern applications and scaling them on the cloud. We innovate to solve customers’ problems, focusing on high‑performance implementations without neglecting the user experience. We recently moved our Product and Tech teams to a pod model, where a pod is a small group (1–4 people) that owns a single mission end‑to‑end: from scoping and building through cleaning, enablement and watching adoption.
The Data Platform and Data Enrichment teams are in charge of making our unified database accessible, usable, enrichable and reliable for all our teams internally. The team owns the Lakehouse that powers Launchmetrics’ data products—a Databricks‑based platform built on Delta Lake, S3,



and following a Bronze/Silver/Gold medallion architecture. This layer stores, enriches, and serves the media intelligence data behind Discover, Genie and Delta Sharing. You’ll work on a batch‑first system that simulates real‑time behavior using Change Data Feed, S3 event triggers, serverless compute and liquid clustering.
Design and build data pipelines (batch and near‑real‑time) using PySpark and Databricks across the Bronze/Silver/Gold medallion layers
Work closely with product, QA and other data engineers to translate enrichment and search requirements into reliable pipelines
Own code quality: structured PySpark jobs, unit tests (pytest) and adherence to team conventions
Participate in cross‑pod initiatives across the data platform
Python, PySpark, Typescript
Data Platform: Storage: AWS S3, MySQL
Cloud: AWS (S3, Kinesis, Lambda, SQS, ECS, Step Functions)
Tools: Jira, Databricks Asset Bundles, Serverless, Claude Code
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📌 Ingegnere dei dati (España)
🏢 Launchmetrics
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