Data Engineer lemlist

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

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