Consultant Data Engineer
Boulevard de Waterloolaan 16, Brussels, Belgique
Sia Partners is a specialist Management Consulting firm in 1999 and has grown into a global firm with approximately 2200 employees and annual revenue exceeding $400m. Our culture is strongly orientated towards high-quality expertise and delivering excellent results and outcomes for our clients, which include a wide range of multinational companies. Our global presence and our expertise in more than 30 sectors and services allows us to accompany clients worldwide. Using innovative technologies such as our own in-house apps, social networks, and digital tools, we provide a truly integrated global service.
Through Consulting for Good, we put our expertise at the service of our clients' climate objectives and make sustainable development a performance lever for our clients. Our portfolio of offerings is cross-sectors, +60 wide consulting offers and generate 15% of revenues.
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 optimization, role management, Infrastructure as Code
- Docker and Kubernetes: containerization & 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 Data Scientists in the learning and provision of their ML models.
- You hold a Master’s Degree with a strong quantitative component (e.g. Econometrics, Physics, Mathematics, Statistics, Quantitative Finance)
- You have 1-3 years of relevant experience in Data, DevOps, Cloud or Software Engineering.
- You have a good level in Python and have a solid foundation in other languages. You are familiar with at least one SQL DBMS (ideally PostgreSQL) and ideally noSQL (ideally MongoDB).
- Ideally, you are already familiar with at least one cloud services platform (GCP, AWS, Azure) with experience mixing different services within the same project. If not, you have a strong appetite to learn more about these topics.
- You are comfortable using Docker, automating CI/CD pipelines and have a strong appetite for gaining competence in Kubernetes if you do not yet have experience with this solution.
- You will be able to work in a DevOps culture, taking responsibility for the code lifecycle, from development to deployment and maintenance of applications (mastery of the Gitlab-CE ecosystem is a plus). You are also familiar with Infrastructure as Code concepts and tools (ideally Terraform).
- You are sensitive to security issues and have experience in implementing application protection solutions (certificate management, HTTPS security, OAuth2 authentication, credential management)
- You have excellent interpersonal and team working skills
- Excellent English, Dutch & French verbal and written skills are required.
Sia Partners is certified "Great Place to Work". Come and join us to take part in this great company.
Sia Partners is an equal opportunity employer. All aspects of employment, including hiring, promotion, remuneration, or discipline, are based solely on performance, competence, conduct, or business needs.