Senior Data Engineer, AI & Data Platform

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.

Position Overview

We are looking for a Data Engineer to join our Data Warehouse team and take ownership of the internal data warehouse and analytics platform at Leadfeeder.

This is a foundational role focused on the internal data layer — consolidating data from across our product, operational systems, and business tools into a reliable, well-structured warehouse that internal teams can build on. The platform you build will be the backbone for analytics, business intelligence, and AI use cases powered by internal data.

Data analysts and business stakeholders depend on the foundations you create: clean, documented, contract-backed datasets that enable them to answer business questions, build analytical models, and run AI-driven workflows without fighting infrastructure. You will shape how data flows across the organisation — defining the standards, tooling, and architecture that make internal data a genuine asset.

Responsibilities

• Design, build, and maintain the internal data warehouse and analytical data layer, consolidating data from across our product and operational systems into a single reliable source of truth.
• Define and enforce data models, schemas, and data contracts so that downstream consumers — data analysts and business teams — can trust and self-serve the data they work with.
• Build and maintain transformation pipelines that turn raw internal data into clean, structured analytical datasets ready for BI, reporting, and AI use.
• Collaborate with Data Analysts to enable AI and machine learning use cases on top of internal data — building the datasets and infrastructure they need to train models and run analytical workflows.
• Implement data quality monitoring, lineage tracking, and observability across the warehouse so issues are caught early and data reliability is maintained over time.
• Work with stakeholders across engineering, product, and business teams to understand their data needs and translate them into scalable, well-documented data models.
• Champion good data engineering practices across the team: CI/CD for data assets, testing, documentation, and reproducibility.