Data Science Developer - Senior

Project Overview

Public Health Ontario (PHO) is building provincial enhanced analytics capability and infrastructure that turns public health data into timely analytics, surveillance, and decision support.

Key Responsibilities

• Participate in product teams to analyze system requirements, and architect, design, code, and implement cloud-based data and analytics products that conform to standards.
• Design, create, and maintain cloud-based data lake and Lakehouse structures, automated data pipelines, analytics models, and visualizations (dashboards and reports).
• Liaise with IT colleagues to implement products, conduct reviews, resolve operational problems, and support business partners in the effective use of cloud-based data and analytics products.
• Analyze complex technical issues, identify alternatives, and recommend solutions.
• Prepare and conduct knowledge transfer.
• Automate the data pipeline for analytic products and advanced analyses, including ingestion, ETL, and production for the foundational capability and for Health Map (DLSPH model data).
• Help identify and prioritize analytic use cases and integrate AI techniques (machine learning, NLP) into PHO’s work.
• Increase PHO’s capacity for modelling, forecasting, and scenario analysis.
• Build on and advance the PHDU and PHO’s Azure data and analytics platform; support data acquisition through data-sharing and governance processes where required.
• Document methods and transfer knowledge to PHO staff so the pipeline and capability can be sustained beyond the engagement.
• Automated data pipelines and cloud data lake / Lakehouse structures supporting the capability and Health Map.
• Analytics models and visualizations (dashboards and reports) conforming to standards.
• Prioritized AI / ML use cases and supporting models (forecasting, scenario analysis).
• Knowledge-transfer documentation and sessions for PHO technical staff.

Qualifications & Requirements

• Must be able to work 2 days onsite per week
• Experience with multiple cloud-based data and analytics platforms and coding / programming / scripting tools to create, maintain, support, and operate cloud-based data and analytics products.
• Experience designing, creating, and maintaining cloud-based data lake and Lakehouse structures, automated data pipelines, analytics models, medallion architecture, and visualizations (dashboards and reporting) in real-world implementations.
• Deep experience with modern technology stacks:
  Azure Storage, Azure Data Lake, Azure Databricks Lakehouse, and Azure Synapse. Power BI, Python, SQL, Azure Databricks, and Azure Data Factory.
• Experience assessing client information-technology needs and objectives.
• Experience problem-solving to resolve complex, multi-component failures.
• Experience preparing knowledge-transfer documentation and conducting knowledge transfer.
• A team player with a track record for meeting deadlines.
• Public sector experience.
• Be an advanced professional able to apply concepts, practices, and procedures in practice.
• Work with minimal direction and lead and train others in technical components and concepts.
• Plan, lead, and deliver complex deliverables that provide options for decisions within the organization.
• Bring a high level of expertise in the required skill set, specialized in the technical area, and provide specific advisory support as required.

• Written and oral communication skills to participate in team meetings, write and edit systems documentation, prepare and present written reports on findings and alternative solutions, and develop guidelines / best practices.
• Interpersonal skills to explain and discuss the advantages and disadvantages of various approaches.
• Experience conducting knowledge-transfer sessions and building documentation for technical staff on architecting, designing, and implementing end-to-end data and analytics products.

Key Skills & Competencies

Azure Storage, Azure Data Lake, Azure Databricks, Azure Synapse, Power BI, Python, SQL, Azure Data Factory, Machine Learning, NLP, ETL, Data Pipelines, Data Lake, Lakehouse, Medallion Architecture