data engineer in B2B SaaS

Leadfeeder turns B2B websites into lead generation engines by revealing which companies visit websites, identifying their interests, and helping marketing and sales teams convert anonymous traffic into qualified pipeline. Its platform connects website behavior with company data, intent signals, and automated workflows.

• Design, build, and operate production data pipelines that power product features, from ingestion through enrichment, processing, and serving;
• Build and maintain streaming and real-time ingestion systems for event data at scale and with low latency;
• Own the cloud infrastructure underpinning the pipelines, including compute, storage, networking, security, and observability, managed as code;
• Collaborate with product and ML engineers to deliver datasets and pipelines for product-facing features and AI/ML workflows;
• Implement data quality, observability, and reliability controls across pipelines;
• Drive engineering practices across the team, including 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.

• 10+ Years of hands-on experience in data and/or software engineering, including a leading role in production data pipelines powering product or customer-facing systems;
• Strong engineering background with production-grade Python, strong SQL, code review, testing, CI/CD, and operational ownership;
• Deep cloud infrastructure experience with AWS or equivalent, including infrastructure-as-code;
• Experience with streaming or real-time data ingestion into a warehouse or lakehouse environment;
• Solid experience with modern data warehouse or lakehouse technologies;
• Hands-on experience with data transformation tooling, particularly dbt;
• Track record of building and operating distributed data systems at scale with attention to performance, reliability, and cost;
• Familiarity with data quality and observability tooling and practices;
• Background in enabling AI/ML workloads on top of production data;
• Strong written and verbal English communication skills;
• Comfortable working in a fully remote environment;
• Physically located within Europe;
• Nice to have: Knowledge of data cataloguing tools, data contracts frameworks, or data mesh principles; background in B2B SaaS and familiarity with intent data, web event data, CRM, product analytics, billing, and support tooling.

• Remote work with a flexible work schedule;
• Mental health support with Auntie;
• Transparent compensation practices: candidates invited to any interview stage receive the salary range and key compensation details before their first conversation.