[Hiring] Senior Data Engineer, AI & Data Platform @Leadfeeder

Role Description

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.

Qualifications

• 10+ years of hands-on experience in data engineering, with demonstrated ownership of production data warehouses or analytical data platforms.
• Strong proficiency in SQL and Python.
• Solid experience with modern data warehouse technologies (Snowflake, BigQuery, Redshift, or similar).
• Experience with AWS data services (S3, Athena, Glue, or equivalents).
• Hands-on experience with data transformation and modelling tools, particularly dbt.
• Experience with workflow orchestration tools such as Apache Airflow or similar.
• Background in enabling AI workloads on top of warehouse data.
• Solid understanding of dimensional modelling, data vault, or other analytical data modelling approaches.
• Familiarity with data quality tooling and testing practices (Great Expectations, dbt tests, or similar).
• Strong communication skills in English, both written and verbal, with the ability to collaborate effectively with non-engineering stakeholders.
• Comfortable working in a fully remote environment.
• Be physically located within Europe.

Nice to have

• Knowledge of data cataloguing tools, data contracts frameworks, or data mesh principles.
• Experience with streaming or real-time data ingestion into a warehouse environment.
• Background in B2B SaaS and familiarity with common product and business data sources (CRM, product analytics, billing, support tooling).

Benefits

• The chance to work with a very knowledgeable, high-achieving and fun team.
• An international, diverse, dynamic and committed work environment.
• The opportunity to work remotely, with a flexible work schedule.
• Mental health support with Auntie.