Data Engineer Consultant
Spring in Africa - Amsterdam, NH, Netherlands
Posted Jun 10, 2026
Benefits
- Parental leave
- Not verified
- Non-birth-parent leave
- Not verified
- Family-building benefits
-
- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
- Not verified
- Relocation assistance
- Not verified
- Childcare support
- Not verified
- Learning budget
- Not verified
- Verification
- Not verified
- Salary
- Not verified
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Market context
- U.S. role benchmark (BLS OEWS)
- $111,944 U.S. median for this role
- Projected growth (BLS Employment Projections)
- +13.7% - Much faster than average
Matched to SOC 15-1252 - Data and ML aggregate by role bucket.
Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.
Schedule
- Shift type
- Not verified
- Weekend work
- Not verified
Application
- Cover letter
- Not verified
- Assessment
- Not verified
- Deadline
- Not stated
Where they hire
State eligibility is not yet verified.
About this role
Data Engineer Consultant Amsterdam, NH, Netherlands Company Description: Sia is a next-generation, global management consulting group born digital, augmented by data, enhanced by creativity, and driven by responsibility. We partner with clients to resolve challenges and capitalize on opportunities. We believe that in today's world of change and disruption, optimism is a force multiplier. Job Description: The Data Science team is young, diverse and growing. As we are still relatively small you will be in the driver seat to shape its future. We have a flat structure and value the entrepreneurial spirit of our members: initiative is highly encouraged. We are also striving for well-being, diversity and knowledge-sharing inside the team, so that we all grow. As a Data engineer consultant you will be working on various client projects as well as on building our own Sia AI solutions, together with our French Data Engineering team: Cloud Services at GCP, AWS and Azure: architecture choices, use of storage & compute services, cost monitoring and optimisation, role management, Infrastructure as Code Docker and Kubernetes: containerisation & orchestration of applications, management of K8s clusters adapted to Data Science workloads Python programming: development of server-side tools (bulk data processing, REST API server, authentication, ...) CI/CD: managing the integration and ongoing deployment of our applications and internal platforms, supporting the deployment of Data Science projects at our clients' sites Data pipelines: development of ETL scripts in Big Data environments Infrastructures & Services adapted to Machine Learning: technology watch and implementation of solutions useful to
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