- We are looking for a Senior Data Platform Engineer to join our BUX team based in Amsterdam
- Our Data & AI team is a lean, international group of engineers dedicated to making BUX a truly data-driven fintech. We pride ourselves on being “platform-first”, building tools that scale rather than just fixing one-off requests
- We like automation, empowering our internal users, and serving our clients in the best way we can. We work closely with other internal and external stakeholders to ensure data is high-quality and actionable
- As a Senior Data Platform Engineer at BUX, you will architect and build the engine that powers our data-driven present and future
- You won’t just be moving bits from A to B; you will be designing the scalable, self-service infrastructure that drives our Data, AI, Machine Learning, and Analytics Engineering efforts
- You will join the Data & AI team, taking ownership of a stack that bridges event streaming with deep analytical warehousing
- Your mandate has two sides: operational excellence - keeping our cloud and data infrastructure reliable, secure, and cost-aware through its full lifecycle - and enabling teams with self-service patterns and strong developer experience
- You will partner with security, compliance, and risk stakeholders so our data platform stays audit-ready and aligned with expectations for financial services and regulated data
- From ensuring high availability across a distributed cloud environment to security and CI/CD pipelines, your work will be the bedrock upon which all BUX intelligence is built
- Design and maintain our core data infrastructure using GCP and Terraform. You take ownership of our network, security, and Kubernetes (GKE) posture, including managing cluster upgrades and resource scaling to ensure it is robust and cost-effective. You take care of private connectivity patterns, firewall rules, DNS, load balancing, and egress controls - so the platform stays defensible and operable
- Develop internal tooling and APIs that empower our data consumers to self-serve, reducing friction between raw data and insights. Also work on features that directly deliver value to our customers
- Refine our ingestion layers - managing the flow from Kafka and Pub/Sub into GCS - while maintaining a smooth, safe deployment and delivery process for platform changes
- Improve workflows in Airflow, and contribute to ensuring the transformation layer is fresh using dbt
- Design and test automated failover and self-healing patterns that withstand regional or major dependency failures. Shape disaster recovery and backup/restore for critical data paths where it matters, and treat observability (metrics, tracing, alerting) and clear SLOs as first-class platform responsibilities—before incidents, not only during them
- Keep a hawk-eye on performance, reliability, and cost across our cloud and warehouse environments; turn signals into durable improvements and operational clarity
Benefits
- Work from anywhere in the world for one month per year
- Unlimited 1-on-1 access to on-demand coaching and mindfulness consultations with psychologists through OpenUp
- A supportive environment that encourages continuous learning and development
- Dedicated training budget for to use towards improving current skills or learning something entirely new
- Celebrations - we make sure to do plenty of fun stuff together, like dinner and drinks or activities like flyboarding, rafting and off-road jeep driving
You have a deep understanding of interconnected data systems and can architect them to fail gracefully while maintaining data integrity across the entire lifecycle. You have 3+ years of experience in data engineering or platform/DevOps engineering, with a track record of building production-grade platforms from the ground up. Deep experience with cloud providers (such as GCP/AWS) managed through Terraform, deploying services on Kubernetes and with serverless or managed services where appropriate. Strong grasp of network architecture (VPCs, segmentation, DNS, load balancing, private connectivity, egress controls) and identity (IAM-style access, workload identity patterns where relevant). You care about encryption, secrets handling, vulnerability-aware operations, and secure CI/CD for infrastructure changes. You bring a developer’s mindset to data, with a deep understanding of testing, version control, API design, and building for scale. We value engineers who stay ahead of the curve by leveraging AI-assisted development tools (like GitHub Copilot or Cursor) and have an interest in or experience with ML platforms like VertexAI. Hands-on experience with Airflow (or similar tools like Dagster or Prefect) is a strong plus for navigating our current environment. Deep, hands-on experience with Python and SQL is a strong plus to help you hit the ground running. Significant experience with Snowflake or similar high-performance warehouses: not just how to query, but how to manage, secure, and optimize them. Familiarity with high-throughput messaging (Kafka, Pub/Sub) and cloud storage (GCS). Experience with dbt for maintaining a clean, version-controlled transformation layer is a significant plus.
