Data Engineer

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

• 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.
• 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.

Additional information

Recruitment process

• 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.