Data Engineer

Data Engineer Join a pod-driven data team at Launchmetrics, building scalable Delta Lake pipelines on Databricks; own end-to-end data enrichment and AI insights. About Launchmetrics Launchmetrics is the leading Brand Performance Cloud used by Fashion, Lifestyle and Beauty (FLB) executives to make smarter decisions around their branding efforts. With over a decade of industry expertise, we have helped more than 1,200 customers create inspiring, impactful and measurable experiences. Founded in NYC and with operating headquarters in Paris, we have employees in ten markets worldwide and offer support in five languages. We have been the trusted brand performance technology to brands worldwide such as Dior, Fendi, Shiseido, NET-A-PORTER and Adidas as well as industry partners like IMG, the Council of Fashion Designers of America, the British Fashion Council, and Camera Nazionale Della Moda Italiana. The Team & the Pods organization We are a team of passionate engineers distributed across the world, always eager to learn new things. We are building state-of-the-art modern applications and scale it 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: A pod is a small group, 1 to 4 people, that owns a single mission end to end: from scoping and building through cleaning, enablement and watching adoption. Pods form around a mission and dissolve when it's done. Everyone is a pod owner, from the most junior person to the most senior, because ownership is the point. Your domain is your product-area home, the part of our software suite you know best. Pods change with every mission; your craft and your domain don't. The role 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 (our client-facing platform), Genie (internal tooling), and Delta Sharing. You'll work on a batch-first system that simulates real-time behavior using primitives like Change Data Feed, S3 event triggers, serverless compute, and liquid clustering. This role plays a key part in our Tech & Product strategy, directly supporting company-wide objectives around customer retention, data trust, and the development of new AI-based insights. What you'll do Design and build data pipelines (batch and near-real-time) using PySpark and Databricks, across the Bronze/Silver/Gold medallion layers Architect efficient Delta Lake table schemas — partitioning/liquid clustering strategy, schema evolution handling, and enrichment workflows 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 Continuously improve pipeline reliability and cost efficiency (OPTIMIZE scheduling, retry/backoff logic, concurrency handling) Participate in cross-pod initiatives across the data platform Technical Stack Languages: Python, PySpark, Typescript Data Platform: Databricks, Delta Lake, Delta Sharing Storage: AWS S3, MySQL Cloud: AWS (S3, Kinesis, Lambda, SQS, ECS, Step Functions, …) Tools: Jira, Databricks Asset Bundles, Serverless, Claude Code Versioning: Git, GitHub CI/CD: GitHub Actions Testing: pytest, Jest What we're looking for Engineer Degree or Master Degree in Computer Science and 3+ years of relevant work experience in full-stack development in a SaaS environment Strong Python and PySpark experience; comfort with distributed data processing at scale Experience with a Lakehouse architecture (Databricks, Delta Lake, or comparable — e.g. Spark on EMR/Glue) Familiarity with medallion architecture patterns (Bronze/Silver/Gold) or similar layered data design Ability to reason about schema evolution, partitioning/clustering strategy, and pipeline reliability (retries, idempotency) Ability to traverse logical sequences of either procedural or object-oriented code, abstracted or static - and understand it entirely Bright, energetic, highly motivated self-starter with experience in a fast-paced, results-oriented organization Ability to adapt, estimate workload, break down a task into logical steps, solve problems, self-improve and suggest new ways of improvement Last, but definitely not least: you speak, read, and write English fluently How we hire Intro call with Talent Acquisition, to get to know each other (30 min). Meet & Greet with VP Software Development. Technical Interview, a live interview with a couple of Software Engineers. Team fit with the engineers and peers you'd build alongside. Why you'll love it here We're a company that puts people first, with a relaxed but genuinely dynamic atmosphere and a team of motivated, curious people who like their work. Autonomy is real here: pods make local decisions and own their outcomes, which means you'll see your fingerprints on the product quickly. You'll get a learning and development allowance, a benefits package tailored to your location, flexible working arrangements with support to set up your home office, and the room to grow along whichever path fits you. We're remote-friendly, with hubs across our twelve markets. OUR COMMITMENT Launchmetrics is proud to be an Equal Opportunity Employer building a diverse and inclusive workforce. If there is anything extra we can do to help you feel at ease during your interview process, please let the PeopleOps team member you’ll be meeting with know. Department Tech & Engineering Role Software Development Locations Girona , Madrid , Paris Remote status Fully Remote Girona Madrid Paris Our Culture We work hard, laugh a lot and collaborate constantly. Life is what happens when you're busy working so we make sure you enjoy it. 👀 See for yourself: