Senior Analytics Engineer

About Lenstra

Lenstra was founded by engineers with a passion for Computer Science and a track record of delivering high-quality solutions. We work at the intersection of Cloud Computing, DevOps, Data Platforms and IT Security, helping top-tier clients in Banking, Insurance, Luxury and Tech solve their hardest technical challenges. On data, we don’t just plug tools together: we define the Data Platform vision with our clients, then build it and integrate it deep into their IT, security and legal constraints. The platforms we deploy are used by large analytics communities, and this role is about making those communities better at their job.

About the role

You will design and own the data models that matter: dimensional models, data marts and semantic layers built with dbt, from business requirement to production.

You will set the analytics engineering standards on your projects: modelling conventions, testing strategy, documentation, code review and CI with GitHub Actions.

You will sit with client business and data teams to turn ambiguous questions (“why are our numbers different from Finance’s?”) into clear metric definitions and models that answer them.

You will gather what analytics engineering teams need from the platform and design the features that solve it, working alongside our data platform engineers (Terraform, Dagster, Elementary for observability).

As technical lead, you will mentor other analytics engineers, review their models and be the reference for modelling decisions. No direct reports: this is a hands-on lead role, and you will still write a lot of SQL.

What we look for

Mindset

Modeller first. You think about grain, keys and slowly changing dimensions before you write the first SELECT.

Teacher by default. You explain why a model is built a certain way, and people come out of a code review knowing more.

Business-curious. You care about what the metric means, not just that the pipeline runs.

Pragmatic. You know when a star schema is right and when a wide table is good enough.

Must-haves

5+ years in analytics engineering, data engineering or BI engineering, with at least 2 years using dbt (or Dataform) in production.

Expert SQL and strong data modelling skills: Kimball dimensional modelling, plus exposure to Data Vault or other approaches.

Hands-on experience with a cloud data warehouse (BigQuery, Snowflake, Databricks or Redshift).

Software engineering habits applied to data: Git, code review, CI/CD, automated tests.

Experience guiding other engineers on standards and best practices.

Fluent French and English: you will present to client stakeholders in both.

Nice-to-haves

Orchestration (Dagster, Airflow) and data observability (Elementary, Monte Carlo).

Semantic layers or metrics stores (dbt Semantic Layer, Cube, LookML).

BI tools such as Power BI, Tableau or Looker.

Python for data tooling.

Consulting experience or experience in banking, insurance or luxury.

Degree: Bachelor’s or Master’s in Computer Science, Engineering, Mathematics, Statistics or a related field.

Hybrid: 2 days remote per week, 3 days onsite (Lenstra office or client premises).

Interview process

Intro call with the recruiter

Technical interview

Final interview with a partner