Director of Data Engineering
New
Based in NetherlandsFull-TimeDirector
SalaryBase salary of $200,000 plus bonus and incentive compensation, depending on experience.
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Job Details
- Experience
- 8+ years of experience in data architecture or data engineering, including at least 3 years of experience leading teams.
- Required Skills
- PythonSQLData engineeringData modelingBigQuerydbt
Requirements
- 8+ years of experience in data architecture or data engineering.
- At least 3 years of experience leading teams.
- Experience building and scaling cloud-based data platforms, using BigQuery or comparable technology.
- Strong hands-on expertise in SQL and Python.
- Experience with dbt or a similar data modeling framework.
- Deep data modeling experience, including designing data structures from business concepts and requirements.
- Experience establishing data quality, governance, monitoring, and observability practices.
- Experience managing sprint-based intake, prioritization, and delivery for a data team supporting multiple stakeholders.
- Familiarity with ELT platforms such as Fivetran or comparable data integration tools.
- Experience with enterprise systems and data integrations; ERP experience is a plus.
- Practical experience using AI agents or AI-assisted engineering tools.
- Direct-to-consumer (DTC) or consumer packaged goods (CPG) experience is preferred.
Responsibilities
- Own the data roadmap, architecture, and company-wide data model.
- Modernize and refactor legacy data jobs to reduce technical debt and support business growth.
- Maintain resilient, reliable, accurate, and consistently available data pipelines.
- Establish data quality, governance, monitoring, and observability practices.
- Manage warehouse and infrastructure costs as data volumes and requirements increase.
- Lead intake and sprint planning, prioritizing requests by business impact.
- Lead, coach, and develop a small data engineering team through code reviews, feedback, and mentorship.
- Contribute hands-on to architecture, engineering, troubleshooting, and platform improvements when needed.
- Integrate enterprise systems, including ERP platforms, into the data warehouse and broader data ecosystem.
- Use AI agents for software development, incident investigation, data quality, and engineering productivity.
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