Leadfeeder
Computer Software
SeniorDataEngineer,AI&DataPlatform
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior Data Engineer, AI & Data Platform at Leadfeeder. Skills: data engineering, data warehouse, analytical data platform, data modelling, SQL, Python, Snowflake, BigQuery, Redshift, AWS, dbt, Apache Airflow, AI workloads. 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”
What You'll Achieve.
enable them to answer business questions, build analytical models, and run AI-driven workflows without fighting infrastructure; make internal data a genuine asset
Industry & Context.
Be physically located within Europe
What They're Looking For.
Must Have
10+ years of hands-on experience in data engineering, demonstrated ownership of production data warehouses or analytical data platforms, proficiency in SQL, proficiency in 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, communication skills in English, both written and verbal, 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, familiarity with common product and business data sources (CRM, product analytics, billing, support tooling)
What You'll Do.
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
Build and maintain transformation pipelines
Collaborate with Data Analysts to enable AI and machine learning use cases
Implement data quality monitoring
and observability across the warehouse
Work with stakeholders across engineering
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
How You'll Work.
Team & Collaboration
Collaborate with Data Analysts to enable AI and machine learning use cases; Work with stakeholders across engineering, product, and business teams to understand their data needs; Champion good data engineering practices across the team; ability to collaborate effectively with non-engineering stakeholders
Communication Scope
communication skills in English, both written and verbal; ability to collaborate effectively with non-engineering stakeholders
Full Job Description
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
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