Senior Data Engineer (Commercial Analytics)

Pleo·United Kingdomsenior

En corto

→Diseñar y mantener pipelines de datos entre herramientas comerciales y BigQuery.
→Gestionar contratos de datos y calidad de datos entre sistemas y equipos.
→Trabajar con RevOps y equipos técnicos para centralizar integraciones y mejorar la confianza en los datos.

Fluent English required for collaboration across global teams.

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¿Qué piden?

✓Experiencia comprobada como Data Engineer en entornos de datos comerciales.
✓Conocimiento avanzado de BigQuery y pipelines de datos en la nube.
✓Capacidad para definir y gestionar contratos de datos con SLAs y propiedad clara.
✓Experiencia con herramientas como HubSpot, Zuora y Vitally en integraciones de datos.
✓Habilidades de colaboración con equipos de RevOps, BAT y Analytics Engineers.
✓Enfoque en calidad de datos, monitoreo y mejora continua desde la fuente.

¿No cumplís todo? Es lo normal — tu dossier gratis te dice qué gaps tenés y cómo cubrirlos en la entrevista.

BigQueryHubSpotZuoraVitallyAirflowdbtGitHubSlackJiraGoogle Cloud

¿A quién escribirle en Pleo?

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About Pleo Messy spend management is tricky business. And tedious processes are a lose-lose situation for all involved, not just finance. At Pleo, we're changing that. 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’. The word ‘Pleo’ actually means ‘more than you’d expect’, and living by that mantra has been the secret to our success over the last 10 years. Now, we’re at a pivotal moment in our journey; every move we make has a direct impact on our 40,000+ customers, our business, and our collective success. We need people who take pride in uncovering customer needs, who turn complex problems into simple solutions, challenge the way things are done (respectfully), and always aim high. With great ambitions driving us forward, we can’t say we’ve got this whole thing figured out. And frankly, that’s half the fun! What we can say is that we’re a driven, progressive, and, importantly, a kind bunch of 850+ people from over 100 nationalities, all committed to delivering the future of business spending, together. About the role Please note: applications will be open until Thursday 24th September 2026 09.00 CEST. We will no review any applications before the closing date so everyone has equal chances to be selected. Use this time to submit a high quality application that will showcase your strengths. We're looking for a Senior Data Engineer to join our Growth Intelligence team at Pleo. In this role, you'll design and maintain the integration architecture and data contracts that connect our commercial and GTM data layer (the pipelines linking our CRM, billing, and customer success tooling to a central BigQuery warehouse) and be part of the team as we scale our commercial data foundations to support RevOps, Customer Success, and the AI products built on top of them. If you're excited about untangling complex, multi-system architecture and are passionate about building data foundations the whole business can trust, then this is the opportunity for you! Who you’ll be working with and reporting to 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. What you’ll be doing 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.

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