Consultant Data Engineer - Google Cloud (GCP)

Company Description

Sia is a next-generation, global management consulting group. Founded in 1999, we were born digital. Today our strategy and management capabilities are augmented by data science, enhanced by creativity and driven by responsibility. We’re optimists for change and we help clients initiate, navigate and benefit from transformation. We believe optimism is a force multiplier, helping clients to mitigate downside and maximize opportunity. With expertise across a broad range of sectors and services, our 3,000 consultants serve clients worldwide from 48 locations in 19 countries. Our expertise delivers results. Our optimism transforms outcomes.

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:

• Google Cloud (GCP): Hands-on experience with Google Cloud computing, data, and AI/ML services, including cloud architecture, compute and storage, IAM, cost optimization, and Infrastructure as Code. Experience applying GCP to data engineering and/or machine learning workloads is required.
• 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.

Qualifications

•  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 a strong interest in data and AI-related topics, the ability to quickly build expertise in these areas, and you’re excited about helping our clients seize the opportunities they offer.
• You have excellent interpersonal and team working skills
• Excellent English and Dutch verbal and written skills are required.

Additional Information

We are unable to provide a work permit for this position

 

Sia 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.