Senior Data Engineer, AI & Data Platform
L
LeadfeederB2B SaaS
Workable locations: Germany
Be physically located within Europe.Full-TimeSenior
Salary not disclosed
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Job Details
- Languages
- Strong communication skills in English, both written and verbal
- Experience
- 10+ years of hands-on experience
- Required Skills
- PythonSQLApache AirflowSnowflakeBigQuerydbtRedshift
Requirements
- 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.
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.
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