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 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 (check our video here https://www.youtube.com/watch?v=nwUEuQXa4Jw)
RECRUITMENT PROCESS
1. Screen CV and interview with Lucas TAM
2. Interview with Eliott – Lead data & Senior Data engineer
3. Live technical interview with Eliott
4. Interview with Mickael – CTO
5. Reference Check & Offer
6. Interview with Charles CEO
