Data Engineer

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 )

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