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 Da