Pleo · jobs.ashbyhq.com · checked today
Staff Analytics Engineer
Messy spend management is tricky business. And tedious processes are a lose-lose situation for all involved, not just finance. We build spend solutions that make managing money seamless, empowering, and surprisingly effective for finance teams and employees alike - with a vision to help all businesses ‘go beyond’.
Skills, with evidence
- Python
Our tech stack currently include: GCP, BigQuery, dbt Core, Airflow, SQL, Python, Claude Code, GitHub Copilot.
must have - SQL
Deep BigQuery and SQL expertise, including performance, cost considerations, and the architectural challenges of complex analytical domains.
must have - dbt
Deep demonstrated expertise in dbt and modelling practices: not just strong modelling, but a formed view of what modelling architecture should look like across a function.
must have - GCP
Our tech stack currently include: GCP, BigQuery, dbt Core, Airflow, SQL, Python, Claude Code, GitHub Copilot.
must have · not practised here - Airflow / orchestration
Our BI stack is also in transition so, you would not be inheriting a mature setup and maintaining it.
- Data modelling
- Set data modelling standards for the function: what clean, layered, well-tested dbt architecture looks like across all domains, at all levels of complexity.
- Governance & security
This is a senior individual contributor role in our Data Services & Governance team where you'll act as the thought and technical leader owning the semantic layer and analytics standards for Pleo.
not practised here - Schema evolution & contracts
- Engage upstream with backend and product engineers to drive data contract discipline and schema ownership upstream, rather than managing inconsistency downstream.
- Dashboards & BI
The semantic layer you will be designing and maintaining will be the single source of truth that AI agents, BI tools, and analysts query.
not practised here - Cost & performance
- Deep BigQuery and SQL expertise, including performance, cost considerations, and the architectural challenges of complex analytical domains.
- Warehousing
- Lead responsible AI-augmented migration work using AI tooling to accelerate data modelling and migrations across the Analytics Warehouse and Operational Data Platform.
- Machine learning
You'll report to the Data Engineering Manager who oversees the Data Infra & MLOps team as well as the Data Services & Governance team.
not practised here
Your plan
- The SQL screen: correct, then fast≈ 2 h
SQL
- Python: the data-wrangling round≈ 4 h
Python
- Data modelling: the round most people fail≈ 4 h
dbt · Data modelling · Warehousing
- Pipeline design: safe to run twice≈ 4 h
Airflow / orchestration · Schema evolution & contracts
- Fourteen months of wrong numbersAdvanced
- The payload changed overnightAdvanced
- You fixed it. Who already used it?Advanced
- Creator payout runAdvanced
- Delete me from everywhereAdvanced
- Spark: read the plan Spark actually ran≈ 2 h
Cost & performance
- applyInPandas per country: what Spark ships to Python, and the native rewriteAdvanced
- Filters you wrote in the wrong place: where Catalyst moves themAdvanced
- snappy, gzip or zstd: measure the Parquet codec trade-off yourselfAdvanced
- Find the four suspects in a slow pipeline (one of them is innocent)Advanced
- A filter after a window function: pushed down or not?Advanced
- Say it out loud≈ 1 h
25 drills · Advanced + Intermediate≈ 17 hours
Not covered by the plan: GCP, Governance & security, Dashboards & BI, Machine learning.
Readiness
Counted from drills you have completed anywhere on D8LooP.
leaves in 3 dremoved the moment Pleo closes it