Lead Analytics Engineer — Build Scalable Data Solutions (Hybrid NL)
Lead Analytics Engineer (permanent, hybrid in the Netherlands) who builds scalable data models and dbt transformation pipelines, defines analytics engineering standards, and mentors engineers in a growing data function. Core stack: SQL, dbt, modern cloud data platforms, with Python and BI tools as pluses.
- Compensation
- Not disclosed
- Location
- Netherlands
- Work setup
- Hybrid
- Seniority
- Lead
- Employment
- Full time
- Experience
- 8+ years
- Category
- Data analytics
- Posted
Skills
Must have
- 8+ years of experience in Analytics Engineering, Data Engineering, or a related data-focused role
- Strong SQL expertise and experience designing production-grade data models
- Proven experience with dbt in production environments
- Experience with modern data stack technologies and cloud-based analytics platforms
- Strong understanding of DataOps principles, including version control, testing, and CI/CD practices
- Experience leading technical initiatives and influencing engineering standards
- Ability to translate business requirements into scalable data solutions
- Strong communication skills with technical and non-technical stakeholders
- Experience mentoring and developing data professionals
Nice to have
- Experience with Python and distributed data processing technologies
- Exposure to data mesh or domain-driven data ownership models
- Experience with analytics engineering platform development or enablement initiatives
- Familiarity with reporting and visualisation tools such as Power BI or Tableau
- Experience driving organisational change and adoption of data best practices
Full description
Lead Analytics EngineerLocation: Netherlands (Hybrid)
Employment Type: Permanent
Overview
We are looking for a Lead Analytics Engineer to join a growing data and analytics function. This is an excellent opportunity for an experienced analytics professional to shape data transformation standards, develop scalable data products, and help build a robust analytics engineering capability across a complex organisation.
You will work closely with data, analytics, and business stakeholders to transform raw data into trusted, reusable datasets that support strategic decision-making. This role combines hands-on technical expertise with leadership, mentoring, and long-term capability building.
Key Responsibilities
- Create and maintain high-quality datasets that support reporting, analytics, and business decision-making.
- Build scalable data models and transformation pipelines using modern analytics engineering practices.
- Partner with stakeholders to understand data requirements and translate them into reliable data products.
- Define and drive analytics engineering standards, best practices, and governance frameworks.
- Own and evolve the technical roadmap for analytics engineering capabilities.
- Lead cross-functional initiatives focused on data quality, scalability, and operational excellence.
- Mentor analytics engineers and support the growth of the wider data community.
- Collaborate with engineering, analytics, and business teams to improve data accessibility and usability.
The ideal candidate would have experience in the following:
(If you do not meet every requirement, we still encourage you to apply.)
- 8+ years of experience in Analytics Engineering, Data Engineering, or a related data-focused role.
- Strong SQL expertise and experience designing production-grade data models.
- Proven experience with dbt in production environments.
- Experience working with modern data stack technologies and cloud-based analytics platforms.
- Strong understanding of DataOps principles, including version control, testing, and CI/CD practices.
- Experience leading technical initiatives and influencing engineering standards.
- Ability to translate business requirements into scalable data solutions.
- Strong communication skills with both technical and non-technical stakeholders.
- Experience mentoring and developing data professionals.
Desirable Experience
- Experience with Python and distributed data processing technologies.
- Exposure to data mesh or domain-driven data ownership models.