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.