Position Overview
The Senior AWS Data and ML Engineer is responsible for designing, implementing, and maintaining scalable data pipelines and machine learning solutions on the AWS platform. This role requires a deep understanding of AWS services, data engineering practices, and machine learning lifecycle management. The candidate will work closely with data scientists, business analysts, and IT teams to deliver high-quality predictive models and data insights to drive business decisions and improve operational efficiencies.
Key Responsibilities
- Design and Implementation of Data Pipelines:
- Design, develop, and maintain scalable, automated, end-to-end data pipelines to ingest, process, and analyze large volumes of data using AWS services like Amazon S3, Glue, Redshift, and Kinesis.
- Implement data lake and data warehousing solutions, ensuring data quality, and accessibility.
- Machine Learning Solutions Development:
- Use AWS SageMaker and other ML services to develop, train, and deploy machine learning models.
- Collaborate with data scientists to operationalize machine learning models, including version control, monitoring, and performance tuning.
- Cloud Infrastructure Management:
- Manage and optimize AWS cloud resources for cost, performance, and security.
- Implement and maintain data security measures, including data encryption, IAM roles, and security groups.
- Collaboration and Leadership:
- Collaborate with cross-functional teams to understand business needs and translate them into technical solutions.
- Build and continuously enhance the organization’s AWS practice by implementing best practices, establishing standards, and driving AWS adoption across projects.
- Design and deliver training programs for new staff on AWS services, tools, and best practices to ensure the team is proficient and up to date with the latest AWS technologies and methodologies.
- Continuous Improvement:
- Stay up to date with the latest AWS services, data engineering, and machine learning trends.
- Evaluate and adopt new technologies to improve existing processes and workflows.
Skills and Qualifications
- Technical Skills:
- Proficiency in AWS services related to data and ML, including but not limited to SageMaker, Glue, Redshift, Kinesis, and Lambda.
- Strong programming skills in Python, Scala, or Java.
- Experience with SQL and NoSQL databases.
- Knowledge of machine learning frameworks (e.g., TensorFlow, PyTorch) and understanding of the ML lifecycle.
- Familiarity with data pipeline and workflow management tools like Apache Airflow.
- Experience with DevOps tools (e.g., Git, Jenkins) for version control and CI/CD pipelines.
- Soft Skills:
- Strong analytical and problem-solving skills.
- Excellent communication and collaboration abilities.
- Leadership skills and experience mentoring team members.
- Education and Experience:
- 5+ years of experience in data engineering and machine learning projects.
- AWS certifications (e.g., AWS Certified Solutions Architect, AWS Certified Data Analytics – Specialty, AWS Certified Machine Learning – Specialty) are highly desirable.
Compensation
- The range offered for this position is: $115-125K annually. The compensation offered within this range is determined by various factors specific to each candidate, such as their skills, qualifications, experience, and credentials.
- Hybrid work structure with 15 days vacation, in addition to provincially observed holidays.
- Competitive benefits package, including extended health and dental, plus life insurance and disability benefits, and healthier life counselling options.
- Opportunities for professional growth and development in a supportive team environment.
- Access to the latest tools and technologies in marketing and design.
Location
Surrey, BC, Canada