About us 👇🏼 lemlist is the sales engagement platform that gives sales teams the unfair advantage they deserve. Bootstrapped since day one, we’ve grown from 0 to $57M ARR in 8 years, without raising a single dollar. Today, we’re a profitable B2B SaaS company, trusted by 40,000+ sales teams worldwide to book more meetings and close more deals. We’re looking for a Data engineer to join our team. You will help design, build and improve scalable data platform to provide data solution to our product. Your main mission will be: • Work collaboratively with the product and business teams to build scalable and agile solutions. • Define our technical standards and take an active part in the structuring data platform architecture decisions and data platform deployment based on data strategic product roadmap • Develop, deploy, and manage highly efficient data platform and automated data pipelines using cloud-based and on-premise technologies. • Design, maintain, and enhance key data product feature 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. What’s in it for you? • Work in a profitable, bootstrapped, and high-growth company that doesn’t rely on external funding to live. • Work on high-impact projects with highly skilled data profiles composed of a Senior Analytics Eng, a Senior Data Scientist and a Senior Data Engineer that directly drive business decisions • Collaborate directly with the C-suite on strategic topics • Work with a team obsessed with speed, growth, and impact. Preferred experience Must have: • 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 is a must. • 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 database such as NoSQL DBMS, Search DBMS, OLAP DBMS • You have a first experience in B2B SaaS Additional information • 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