Senior Data Engineer (Commercial Analytics)

You’ll report to the Senior Manager of Growth Intelligence and work closely with the RevOps and the BAT (Business Architecture & Technology) teams to understand what commercial operations needs from data. Our team is highly collaborative and dedicated to keeping Pleo's commercial and GTM data trustworthy and well-governed. You’ll also partner with Analytics Engineers to ensure pipeline outputs are modelling-ready, and engage upstream with tool owners and engineering teams to push data quality and schema discipline closer to the source.

As a Senior Data Engineer, you will:

Design, build, and maintain data pipelines between commercial tools (HubSpot, Zuora, Vitally) and our BigQuery data warehouse.

Own reverse ETL flows: enrich or define data in the warehouse and push it back into operational tools like HubSpot, keeping definitions consistent across systems.

Implement and maintain data contracts with clear ownership, SLAs, and schema agreements between data producers (tool owners, product engineers) and data consumers (analysts, RevOps, AI tooling).

Partner with RevOps and the BAT team to understand operational data requirements and translate them into reliable, governed pipelines.

Build monitoring and alerting that catches data quality issues before they surface in dashboards, operational reports, or downstream models.

Simplify integration architecture by centralising data flows through the warehouse, reducing point-to-point tool integrations and the maintenance burden that comes with them.

Work with Analytics Engineers to ensure pipeline outputs are structured and documented for downstream modelling.

Engage upstream with tool owners and engineering teams to move data quality and schema discipline closer to the source.

Our commercial data stack connects subscription billing (Zuora), CRM (HubSpot), and customer success tooling (Vitally) to a central BigQuery data warehouse. You can expect to work with the following tech stack: BigQuery, Python, HubSpot, Zuora, and Vitally plus dbt, Fivetran/Census, or Airflow depending on your background.

You’ll thrive in this role if you have:

Proven experience building and maintaining production data pipelines in a commercial or GTM data environment.

Strong understanding of CRM data models, APIs, and field management (HubSpot or comparable experience).

Hands-on experience with reverse ETL patterns: pushing warehouse-enriched or warehouse-defined data back into operational tools.

Extensive BigQuery experience.

Experience implementing data contracts in a production environment.

Python proficiency for pipeline development, transformation logic, and tooling.

Git-based workflows and CI/CD experience for data engineering code.

This role is a good fit for you if:

You are equally excited by deep technical work as you are by collaborating with business stakeholders.

You can comfortably work with a wide variety of data sources and can operate within a complex multi-system environment.

You truly shine at solving problems and driving alignment between GTM/commercial stakeholders.

This role is not a good fit for you if:

You prefer to work on platform matters that are one step removed from the business.

You have never collaborated with senior stakeholders on GTM / commercial analytics work.

You need a very defined roadmap and backlog to operate efficiently. You'll need to adapt to rapid changes and be part of the roadmap definition discussions in this role.

In your first 6 months at Pleo, you’ll:

Become familiar with our data ecosystem and codebase.

Support to drive a HubSpot reimplementation project from the data side.

Collaborate with Data Platform & Analytics Engineers as well as GTM teams to improve or commercial analytics efforts.

Please note: We can hire on a remote, hybrid or in-person set-up in any of the locations listed on the advert but you will need to be physically based in the country of your choice with a valid right to work . We are unable to offer visa sponsorship for this role in any of the listed locations.

We want to ensure you are set-up for success and understand what will be expected of you. If your application is successful, our interview process is as follows:

Intro call: A 30-minute chat with our Talent Partner to discuss the role and your background.

Core skills tests: An async coding exercise through our CoderPad third party platform.

Hiring Manager interview: A 60-minute chat with your Manager to deep dive into your technical experience and impact.

Pleo Challenge: A 75-minute live interview with two colleagues to assess your architecture and problem solving skills.

Transparency is important to us so we also wanted to share some insights about what we’re looking for in applications to ensure you can set yourself up for success!

Last time we hired a Senior Data Engineer, we received a total of 543 applications but only 15 were selected for an intro call. With this in mind, we want to help you understand what we really care about when reviewing your application:

We receive a lot of CVs, and many of them are AI-generated. We love seeing people leverage AI—it’s a big focus for us internally too—but without human intervention, these CVs can sometimes become generic and fail to show a candidate in the best light. What we're really looking for is the specific details of real impact that only you—not AI—know from your previous experience. A top tip from us is to use the “Achieved X, as measured by Y, by doing Z” formula (credit: Laszlo Bock, ~2014) to give a really clear picture of what you’ve worked on. A final note: including links to your previous companies' websites is a huge help and allows us to truly understand your background.

Every single application we receive is reviewed by a human (yes, hundreds of them) because we believe that candidates' efforts should be matched by an equal level of human care. This means that we expect a similar level of attention put into your application. Read and answer the application questions carefully, they make a huge difference in our decision-making process.

This role requires experience operating within a SaaS organisation, collaborating closely with GTM teams. It is equally important to demonstrate your technical expertise with production pipelines as it is to demonstrate your understanding and impact across commercial analytics.