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Senior Manager, AI and Data Platform Leader

1 month ago

Toronto, Ontario, Canada WAVE Full time
About the Role

The Senior Manager, AI and Data Platform is a key leader at Wave, responsible for ensuring the reliable and efficient operation of both data and AI systems. This role requires deep expertise in data platforms, DataOps, and AI/ML infrastructure. The successful candidate will manage a team of Data Engineers and ML Engineers, driving the development, deployment, and monitoring of AI models and a modern CDC-based data stack. Additionally, this role includes oversight of data and AI operations, ensuring that models perform optimally and that our infrastructure remains robust and future-proof.

Key Responsibilities:
  • Lead the development and optimization of a secure, scalable AI and data platform that supports Wave's growth, managing data ingestion, our data lake, and AI/ML infrastructure on AWS.
  • Lead the adoption and integration of DataOps practices to automate and optimize data workflows, ensuring data quality, lineage, and availability across the platform.
  • Continuously evolve Wave's data, DataOps, and AI/ML architecture by reviewing, recommending, and implementing changes, integrating new tools, and ensuring thorough documentation.
  • Oversee the full lifecycle of ML models, including training, deployment, monitoring, and optimization, while managing our portfolio of generative AI applications.
  • Collaborate closely with internal technology partners (Compliance, Engineering, IT, Security) and external partners like AWS to align initiatives with organizational goals.
  • Ensure data and AI model quality, lineage, and integrity across the platform through robust systems and processes.
  • Implement and maintain governance processes that address stakeholder needs, emphasizing security, transparency, and efficiency.
  • Lead the team in scoping, prioritizing, and executing data and AI projects, setting clear goals, tracking progress, and removing obstacles.
  • Mentor the team in best practices for data engineering and AI/ML operations, conducting regular 1-on-1s, setting goals, and holding the team accountable.
  • Represent the team with confidence and transparency, managing relationships across all data and AI functions.

You Thrive Here By Possessing the Following:
  • 7+ years in data and AI/ML, including 5+ years in data platform architecture (data pipelines, governance, and architecture).
  • 5+ years managing teams of data and/or AI/ML professionals, with a track record of leading cross-functional teams and complex projects.
  • Advanced SQL skills for querying large, complex datasets.
  • Proficient in Python, with experience in data frame-based libraries (Pandas, Dask, Polars) and AI/ML libraries (TensorFlow, PyTorch, Scikit-learn).
  • Strong understanding of DataOps practices, including automated data pipelines, data quality monitoring, and CI/CD for data.
  • Experienced with AWS services (S3, Redshift, RDS, DynamoDB, SageMaker).
  • Strong understanding of distributed computing (Apache Spark) and hands-on experience with AWS Glue.
  • Solid grasp of MLOps practices, including CI/CD for AI/ML and maintaining evolving data and AI system architectures.
  • Familiar with data pipeline tools like Kafka, dbtCloud, Terraform, and Kubernetes.
  • Expertise in DevOps and Infrastructure as Code (IaC) with Terraform HCL.
  • Proven ability to collaborate with internal partners (Compliance, Engineering, IT, Security) and external partners (AWS) to align initiatives with organizational goals.
  • Experienced in Agile team leadership using Scrum, with skills in sprint performance reporting, backlog grooming, and alignment with OKRs and KPIs.
  • Success in setting, meeting, and exceeding quarterly OKRs, with consistent KPI reporting.
  • Committed to mentoring through regular 1-on-1s, setting goals, and providing constructive feedback.
  • Effective communicator, able to convey complex technical matters visually and verbally.
  • Proactive, autonomous project manager with a strong ability to partner with business and engineering stakeholders from ideation to execution.

Nice to Have:
  • Generative AI Expertise: Expertise in generative AI, including understanding key techniques and applications.
  • RAG, LLM, and Agentic Patterns: Familiarity with Retrieval-Augmented Generation (RAG), Large Language Model (LLM) fine-tuning, and agentic GenAI patterns.
  • AWS Bedrock and LangChain: Experience deploying and managing generative AI with AWS Bedrock, LangChain, or similar platforms.
  • Data Privacy and Compliance: Experience implementing data privacy and compliance solutions, especially with AI Management Systems like ISO 42001.
  • Third-Party Tools: Experience with Stitch, Segment, and integrating third-party data tools into platforms.
  • Cross-Industry Experience: Knowledge or experience in Payroll, Banking, Fintech, Payments Processing, or Accounting industries where data and AI/ML are critical.
  • Data Product Management: Experience managing data product lifecycles, including versioning, monitoring, and continuous improvement.