[Hiring] Data Engineer @lemlist

Role Description

We’re looking for a Data Engineer to join our team. You will help design, build, and improve a scalable data platform to provide data solutions to our product.

• Work collaboratively with the product and business teams to build scalable and agile solutions.
• Define our technical standards and take an active part in structuring data platform architecture decisions and deployment based on the data strategic product roadmap.
• Develop, deploy, and manage highly efficient data platforms and automated data pipelines using cloud-based and on-premise technologies.
• Design, maintain, and enhance key data product features to ensure they are high-quality, certified, and easily accessible/integrable by enterprise users, components, and systems.
• Analyze and develop data operations and pipelines in line with enterprise guidelines and best practices (e.g., data quality processes, governance, and deep catalog/glossary curation).
• Continuously adapt to evolving requirements by maintaining and improving existing data pipelines integrating new features and change requests using an agile approach.
• Ensure data quality, lineage, versioning, and observability across the whole stack.
• Support CI/CD and release processes.

Key Results

Within 3 months, you will have/be:

• Successfully onboarded and integrated into the team.
• Onboarded our existing data platform end to end: sources, ingestion jobs, warehouse models, orchestration, BI layer, and who consumes what.
• Delivered a written audit of the current stack — what works, what's fragile, what's redundant, what's undocumented — with a severity ranking and estimated cost of each gap (reliability, cloud spend, engineering time, business risk).
• Shipped at least one visible quick win: a broken or unreliable pipeline fixed, a cost anomaly resolved, or a critical dataset made trustworthy.
• Turned the audit into an agreed technical roadmap: proposed target architecture, tech choices (warehouse, streaming, orchestration, transformation), and a migration path with trade-offs made explicit and validated with Product, Data, and the C-suite.
• Improved our data engineering standards: repo structure, Git workflow, CI/CD for data, environments, code review, and deployment process. New pipelines follow them without needing to be told.

Within 12 months, you will have:

• Participated actively in the improvement of our data platform in order to scale with data volume and product growth without recurring firefighting, and cost per pipeline is understood and controlled.
• Cut incident volume and time-to-detect on critical datasets to a level where business teams trust the data by default.
• Put observability in place: freshness, volume, and schema checks with real alerting on our critical datasets, plus documented SLAs and clear ownership.
• Unlocked new use cases the business couldn't previously ask for: proposed and shipped platform capabilities that opened up work in product analytics, in-product data features, or ML/AI enablement for the Data Scientist.
• Become an additional reference on our data architecture — the person the C-suite (CEO, CPO, CMO, Head of Sales) and Product consult before committing to decisions with a data dependency.

Qualifications

• Master's degree in computer science, distributed systems, data engineering, engineering, or equivalent.
• 5+ years experience in intensive data platform in the context of Big Data and cloud infrastructures/platforms.
• Strong background in Big Data architecture approaches and DBMS/Data Warehouse modelling, optimisation, and management.
• Deep knowledge of SQL, Python, and Spark-related programming languages.
• Experience with data warehouses and lakes (BigQuery, Snowflake, Databricks, Storage, Delta lake…).
• Extensive expertise in data preparation, integration, modelling, and governance processes.
• Proven experience in designing and managing end-to-end production-ready solutions.
• Solid experience in developing, optimising, and maintaining scalable data ingestion and transformation pipelines using modern data technologies - including streaming tools (Pub/Sub, Kafka).
• Familiarity with DataOps know-how: Git, Docker, CI/CD practices (Jenkins) and deployment workflows in a data engineering environment.
• Experience in ensuring data quality, consistency, and performance across data platforms, while applying data governance principles.
• Strong analytical mindset, with the ability to solve complex data challenges and continuously improve data solutions.
• Fast learner, high ownership, structure, and execution speed. Demonstrated ability to thrive in a demanding, fast-growing environment.
• Fluent in French and English.

Nice to have

• Hands-on experience on applicative databases such as NoSQL DBMS, Search DBMS, OLAP DBMS.
• First experience in B2B SaaS.

Benefits

• Competitive salary and company bonus (up to 18K€ per year depending on company’s performance).
• 38 days of holidays/year.
• Alan Blue: Comprehensive 100% premium medical coverage for you and your family.
• Swile Meal Tickets: Enjoy daily meal tickets to fuel productivity.
• Navigo Card: Seamless commuting with a 100% covered Navigo card.
• Gear: Get the laptop, tools, and equipment you need for your job.
• Team building: We all meet once per year at really cool places around the world.

Recruitment process

• Screen CV and interview with Lucas TAM.
• Interview with Eliott - Lead Data & Senior Data Engineer.
• Live technical interview with Eliott.
• Interview with Mickael - CTO.
• Reference Check & Offer.
• Interview with Charles - CEO.