Senior Data Engineer, Platform Data

Leadfeeder turns B2B websites into lead generation engines. Every day, potential buyers visit your website and leave without filling out a form. Leadfeeder reveals which companies are behind that traffic, shows what they care about, and helps teams act while interest is high.

By connecting website behavior with company data, intent signals, and automated workflows, Leadfeeder helps marketing and sales teams prioritise the right accounts and turn anonymous traffic into qualified pipeline.

We're a remote-first, international team building the next generation of lead generation technology for B2B marketers. Join us and help redefine how B2B companies generate leads from the signals already happening on their website.

The Role

We are looking for a Senior Data Engineer to join our Platform Data team and take a leading role in our production data pipelines — the systems that power Leadfeeder's product experience, from ingesting and enriching web visit signals to delivering intent data and account intelligence to customers in near real time.

This is a deeply technical, engineering-led role focused on the production data layer — the pipelines, streaming infrastructure, and cloud systems that turn raw signal into product. Reliability, scale, and engineering quality are the bar. The platform you build is what customers experience every day.

You will work closely with product and engineering teams to design, build, and operate the pipelines, streaming systems, and cloud infrastructure that move data through Leadfeeder at scale. You will shape how we ingest, process, enrich, and serve production data — defining the standards, tooling, and architecture that make our data platform a competitive advantage.

Responsibilities:

• Design, build, and operate production data pipelines that power Leadfeeder's product features — from ingestion through enrichment, processing, and serving.
• Build and maintain streaming and real-time ingestion systems that move event data through the platform at scale and with low latency.
• Own the cloud infrastructure underpinning the pipelines — compute, storage, networking, security, observability — designed and managed as code.
• Collaborate with product and ML engineers to deliver datasets and pipelines that power product-facing features and AI/ML workflows.
• Implement data quality, observability, and reliability controls across the pipelines so issues are caught early, incidents are short, and downstream teams can trust the data.
• Drive engineering practices across the team: code review, testing, CI/CD for data, infrastructure-as-code, performance tuning, and cost discipline.
• Partner with engineering, product, and ML teams to translate product requirements into scalable, well-documented data systems.