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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Engineer, AI & Data Platform - **Company:** LEADFEEDER INC - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Amazon S3, Data Analysis, BigQuery, Continuous Integration, Data as a Services, Information Engineering, Data Infrastructure, Data Transformation, Data Vault Modeling, Data Warehousing, Dimensional Modeling, Python (Programming Language), Operational Databases, SQL Databases, Data Streaming, Usage Analysis, Snowflake, Data Layers, Data Lineage, Modeling and Simulation, Real Time Data, Operational Systems, Data Management, Amazon Redshift - **Published:** May 15, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=b9c87ec5354ee008 ## About the Role Do you have experience in Stakeholder engagement?, * 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., * 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). ## 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., * 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. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [WeAreDevelopers LIVE - CSS is DOOMed](https://www.wearedevelopers.com/videos/1838-wearedevelopers-live-css-is-doomed) - [Data Governance in the Era of AI](https://www.wearedevelopers.com/videos/1622-data-governance-in-the-era-of-ai) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [Making Data Warehouses fast. 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