Switchgrowth Com
Customer Engineer, Analytics and ML Products
Toronto, CAN
Sponsorship not specifiedDetected 426 days ago
PythonRubyFull-Stack DevelopmentSQLBigQueryDockerKubernetesDevOpsMachine LearningData EngineeringData ScienceData VisualizationMLOpsA/B TestingProduct ManagementProduct StrategyAgileMarketing AnalyticsCommunication
About the role
- In this role, you'll be the bridge between technical challenges and client needs-translating real-world data problems into scalable analytics solutions and intelligent ML features.
- Your work will directly impact the way clients experience and derive value from Switch's data and ML products.
- This is a technical role that leans heavily on data engineering, data science, analytics, and ML infrastructure, while still involving front-end and back-end development and product management.
Responsibilities
- Full-Stack + Data & ML Development: Work across the full tech stack-both for building user-facing features and analytical tools.
- You'll design scalable data pipelines, integrate ML models into production systems, and support experimentation frameworks (e.g., incrementality testing).
- Collaborative Innovation with a Data Focus: Partner closely with our business team to uncover insights from client data, identify common analytical needs, and proactively develop tools like custom dashboards, measurement models, and centralized ML infrastructure.
- If you're someone who thrives at the intersection of data, engineering, and customer impact, and you're excited to create intelligent data systems and client-facing ML features, we'd love to talk to you.
- Join us in helping our clients solve complex marketing and analytics problems, while building a team rooted in technical excellence and data-driven decision-making.
Requirements
- A Bachelor's degree in Computer Science, Engineering, Data Science, or a related field.
- At least 2 years of full-stack development experience and 5+ years in analytics, data science, or ML-focused roles.
- Proficiency in SQL and Python, and familiarity with data platforms like BigQuery, Vertex AI, or similar tools.
- Demonstrated experience designing and maintaining scalable, reliable data pipelines and ML models.
- Familiarity with tools like Looker/Looker Studio for reporting, and experience with marketing experiments, ROI modelling, or statistical analysis.
- Experience with agile methodologies, DevOps, and optionally MLOps/DataOps workflows.
- You can independently fix data bugs and write simple predictive models.
- Containerization (Docker/Kubernetes), experience with digital marketing analytics, and comfort in startup environments.
Skills
- Your stack may include SQL, BigQuery, Python (for analytics/ML), and Ruby or modern front-end frameworks.
Benefits
- Bonus: Containerization (Docker/Kubernetes), experience with digital marketing analytics, and comfort in startup environments.
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