Position Overview
As a Cloud Data & AI Engineer at Slalom, you will collaborate with cross-functional teams to design and implement Google Cloud data and AI solutions. You will work with clients to solve their most complex and interesting business problems, leveraging your expertise in data engineering and AI engineering.
Key Responsibilities
- Design, build, and operationalize large-scale enterprise data and AI solutions using Google Cloud services such as BigQuery, Vertex AI, Dataflow, Cloud Storage, Pub/Sub, and more.
- Implement cloud-based data solutions for data ingestion, transformation, and storage; and AI solutions for model development, deployment, and monitoring, ensuring both areas meet performance, scalability, and compliance needs.
- Develop and maintain comprehensive architecture plans for data and AI solutions, ensuring they are optimized for both data processing and AI model training within the Google Cloud ecosystem.
- Provide technical leadership and guidance on Google Cloud best practices for data engineering (e.g., ETL pipelines, data pipelines) and AI engineering (e.g., model deployment, MLOps).
- Conduct assessments of current data architectures and AI workflows, and develop strategies for modernizing, migrating, or enhancing data systems and AI models within Google Cloud.
- Stay current with emerging Google Cloud data and AI technologies, such as BigQuery ML, AutoML, and Vertex AI, and lead efforts to integrate new innovations into solutions for clients.
- Mentor and develop team members to enhance their skills in Google Cloud data and AI technologies, while providing leadership and training on both data pipeline optimization and AI/ML best practices.
Required Qualifications
- Proven experience as a Cloud Data and AI Engineer or similar role, with hands-on experience in Google Cloud tools and services (e.g., BigQuery, Vertex AI, Dataflow, Cloud Storage, Pub/Sub, etc.).
- Strong knowledge of data engineering concepts, such as ETL processes, data warehousing, data modeling, and data governance.
- Proficiency in AI engineering, including experience with machine learning models, model training, and MLOps pipelines using tools like Vertex AI, BigQuery ML, and AutoML.
- Strong problem-solving and decision-making skills, particularly with large-scale data systems and AI model deployment.
- Strong communication and collaboration skills to work with cross-functional teams, including data scientists, business stakeholders, and IT teams, bridging data engineering and AI efforts.
- Experience with agile methodologies and project management tools in the context of Google Cloud data and AI projects.
- Ability to work in a fast-paced environment, managing multiple Google Cloud data and AI engineering projects simultaneously.
- Knowledge of security and compliance best practices as they relate to data and AI solutions on Google Cloud.
- Google Cloud certifications (e.g., Professional Data Engineer, Professional Database Engineer, Professional Machine Learning Engineer) or willingness to obtain certification within a defined timeframe.
Benefits & Perks
- Meaningful time off and paid holidays
- Parental leave
- 401(k) with a match
- Highly subsidized health, dental, and vision coverage
- Adoption and fertility assistance
- Short/long-term disability
- Yearly $350 reimbursement account for any well-being-related expenses
- Discounted home, auto, and pet insurance
- Target base salaries ranging from $96,000 to $217,500 depending on location and role
- Eligibility for an annual discretionary bonus
- Opportunities for professional development and growth
