Lead Cloud Data Solutions Engineer – HR Systems (Banking)

Lead Cloud Data Solutions Engineer – HR Systems (Banking)

 

Location: Toronto, ON
Work Model: Hybrid – 4 Days Work From Office (WFO)

Role Overview

As the Lead Data Solution Engineer within the Data Strategy team, you will architect and deliver enterprise-scale data solutions that drive innovation and efficiency across HR processes.

This role combines deep technical, hands-on expertise in data engineering, data architecture, and data modeling with strategic leadership to align data systems with HR business objectives. You will lead cross-functional teams, mentor engineers, and ensure compliance with regulatory standards in a highly governed environment.

Key Responsibilities

• Design and develop data models, processes, and systems, ensuring alignment with business requirements and organizational standards and best practices.
  
• Provide hands-on technical leadership and deliver innovative data solutions that enhance operational efficiency and address complex business challenges.
  
• Lead end-to-end data solution design and implementation for HR systems, ensuring scalability, security, and alignment with hybrid cloud strategy (Azure / AWS / On-Premises).
  
• Define and govern data architecture standards, including:
  
  • Enterprise data models
    
  • Relational database design
    
  • Metadata management for HR domains
    
• Build and optimize scalable ETL/ELT workflows using technologies such as:
  
  • OpenShift
    
  • Snowflake
    
  • dbt
    
  • Airflow
    
  • Spark
    
• Focus on HR data integration across domains such as:
  
  • Core HR
    
  • Payroll
    
  • Talent Management
    
• Drive DevOps and CI/CD practices to automate deployment pipelines and ensure high availability of HR data platforms.
  
• Collaborate with HR stakeholders and product managers to translate business requirements into technical solutions.
  
• Ensure compliance with data governance and regulatory frameworks, including risk and privacy requirements.
  
• Mentor engineers and lead cross-functional teams consisting of developers, business analysts, and HR partners.
  
• Apply emerging technologies such as AI/LLM and advanced analytics to enhance HR data capabilities and innovation.
  

Required Qualifications & Experience

Education

• Bachelor's degree in Computer Science, Software Engineering, or equivalent.
  

Professional Experience

• 10+ years of experience in data architecture, data engineering, or enterprise data management.
  
• 6+ years designing enterprise data architectures across multiple domains.
  
• 6+ years designing and optimizing ETL/ELT and data integration solutions.
  
• 6+ years working with data warehouses, data lakes, and analytics platforms.
  
• 6+ years of experience with data modeling and relational database design.
  
• 5+ years of hands-on experience with Snowflake or similar cloud data platforms.
  
• 5+ years of hands-on experience with SQL, Python, Java, or similar programming languages.
  
• 3+ years of experience designing, architecting, or building data products.
  

Technical & Domain Skills

• Strong understanding of data governance frameworks.
  
• Strong knowledge of data quality methodologies and metadata management.
  
• Experience working in highly regulated industries, particularly banking or financial services.
  
• Experience designing scalable, secure, and high-performance data solutions.
  
• Strong understanding of enterprise data architecture, data integration, and data engineering best practices.
  
• Experience with cloud and hybrid data environments, including Azure, AWS, and on-premises infrastructure.
  
• Strong technical leadership, mentoring, and cross-functional collaboration skills.
  

Key Technology Stack

Data Platforms: Snowflake, Data Warehouses, Data Lakes
Data Engineering: ETL, ELT, Data Integration, Spark
Data Transformation: dbt
Workflow Orchestration: Airflow
Container / Platform: OpenShift
Programming: SQL, Python, Java
Cloud: Azure, AWS, On-Premises
DevOps: CI/CD, Deployment Automation
Emerging Technologies: AI/LLM, Advanced Analytics
Data Management: Data Modeling, Data Architecture, Metadata Management, Data Governance, Data Quality

Core Competencies

• Enterprise Data Architecture
  
• Data Engineering
  
• Data Modeling
  
• Data Integration
  
• ETL/ELT
  
• Cloud Data Platforms
  
• Data Warehousing & Data Lakes
  
• Data Governance
  
• Metadata Management
  
• Data Quality
  
• HR Data Integration
  
• DevOps & CI/CD
  
• Technical Leadership
  
• Cross-Functional Team Leadership
  
• Regulatory Compliance