Manager, Enterprise Data Solutions

Interested in joining one of Canada’s top performing asset managers?

We are looking for a Manager, Enterprise Data Solutions to help build and deliver scalable data capabilities across the organization.

Working closely with the technology leadership including Senior Manager, Enterprise Data Solutions, this role will provide both technical, strategy and people leadership across our enterprise data environment. You will help translate our data strategy into practical solutions, lead important data initiatives, and develop strong partnerships across investment, operations and technology teams.

A key part of this role is also helping us build greater leadership depth and operational resilience within Enterprise Data Solutions. The successful candidate will develop broad knowledge across our data platforms, architecture and key business processes, and be capable of assuming leadership responsibility across important initiatives when required.

Our offices currently operate on a hybrid model with three days in office.

What You Will Do

• Data Strategy Execution: Work with the Senior Manager to translate the enterprise data strategy into a practical roadmap, priorities and deliverables. 

• Data Architecture & Solutions: Lead the design and implementation of scalable data solutions, data models, integration patterns and platforms supporting investment and enterprise use cases. 

• Delivery Leadership: Take ownership of significant data initiatives from design through implementation, coordinating engineers, analysts, business partners and technology teams. 

• AI & Advanced Data Capabilities: Help integrate AI, machine learning and intelligent automation into enterprise data solutions where they provide meaningful business value. 

• Data Platforms: Support the implementation and evolution of enterprise data platforms, including data lakes, analytics environments and cloud-based data services. 

• Data Governance & Controls: Help establish practical standards for data quality, ownership, lineage, controls and responsible use of AI and data. 

• Business Partnership: Work closely with investment management, operations and technology teams to understand business requirements and translate them into sustainable data solutions. 

• Team Leadership: Lead, coach and develop data professionals while creating clear accountability, strong engineering practices and a collaborative team environment. 

• Leadership Depth & Resilience: Develop working knowledge across critical Enterprise Data Solutions responsibilities, platforms and stakeholder relationships so that leadership and decision-making can continue effectively when priorities or availability change. 

• Continuous Improvement: Identify opportunities to simplify the data environment, reduce manual processes, improve reliability and increase reuse across the organization. 

What You Bring 

• Bachelor’s or master’s degree in Computer Science, Data Science, Engineering, Information Systems or a related discipline. 

• 7+ years of relevant data and technology experience, ideally within Investment Management, Financial Services or a complex enterprise environment. 

• 4+ years of experience delivering modern data platforms or enterprise data solutions into production. 

• Experience leading technical teams, projects or significant workstreams with accountability for delivery. 

• Strong understanding of modern data architecture, data modelling, integration and cloud-based data platforms such as Azure, Microsoft Fabric or AWS. 

• Practical understanding of AI, machine learning and intelligent automation and how these capabilities can be incorporated into enterprise data solutions. 

• Working knowledge of programming and data technologies such as Python, SQL or comparable tools. 

• Strong understanding of data governance, data quality, controls and enterprise data management practices. 

• Ability to work effectively with both senior business stakeholders and technical teams, translating between business needs and technology solutions. 

• Strong leadership, communication, problem-solving and analytical skills. 

• Experience within investment management data domains such as portfolio, security, transaction, reference, market or operational data would be an asset. 

What Success Looks Like

Success in this role will mean that we have:

• Enterprise data initiatives move from design into production with clear ownership, strong delivery discipline and measurable business outcomes. 

• The modern data platform continues to mature into a scalable, reliable and well-governed enterprise capability. 

• Data solutions increasingly use common architecture, integration patterns, reusable components and shared data products rather than bespoke approaches. 

• Data quality, lineage, observability, security and operational readiness are built into solutions from the outset. 

• The team operates with clear priorities, strong accountability and effective engineering practices. 

• Business, investment and technology teams have confidence in EDS as a delivery partner and technical authority for enterprise data solutions. 

• Technical debt, duplicate pipelines, manual processes and unnecessary platform complexity are progressively reduced. 

• Enterprise data capabilities become easier to operate, support and scale across the organization.

• AI, machine learning, RAG and automation initiatives are enabled through trusted, governed and entitlement-aware enterprise data.

• The Manager helps build team capability and practical understanding of how AI can be safely and effectively incorporated into enterprise data solutions.

The expected base salary range for this position is $135,000 – $165,000. The midpoint represents our general market target for a fully qualified and experienced individual performing the role. Actual starting salary may fall below or above the midpoint depending on the candidate's relevant experience, skills and qualifications, as well as internal equity considerations. Further questions may be directed to the HR team during the interview process.

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