Manager, Data Management and Analysis

Key Skills

Microsoft-Excel, Java Programming, Design, Python Programming, SQL, VB+, PowerBI, Data Visualization, Data Modeling, Database testing, C# Programming, ETL frameworks, Data Analysis

Job Description

Manulife’s Global Wealth & Asset Management (Global WAM) segment has embarked on an exciting journey that is transforming the organization by harnessing the power of data and related analytics. This transformation is a key strategic enabler and we are looking for individuals who are passionate about joining the team that will lead this journey.

Overview of the Role

The Chief Data Office in close partnership with key business stakeholders will support the transformation of the organization to ensure data is leveraged as a strategic asset supporting our business goals. The group’s objectives include:

• A single, central source for data which is easy to access and describes how the data was obtained
• Data quality continually monitored and accuracy improved
• Common standards and policies for data governance
• Flexible reporting tools to access data and adapt to changing business or regulatory requirements

The position is primarily responsible to support the Data Management team with BAU activities and ongoing initiatives, conducting data analysis, preparing dashboards, and making recommendations focused on cost optimization and strategic operational insights. This includes data sourcing and cleansing, identifying key metrics, performing analytical deep-dives, developing reporting packages, but also building an expertise around Data Management processes, and identifying new ways to enhance data quality and operational efficiency.

The incumbent is required to have robust technical expertise coupled with strong communications skills. We’re looking for a self-starter with both a strong sense of curiosity for uncovering trends and insights and enthusiasm for sharing these observations to ultimately influence business decisions and help drive fragmented market data costs down.

Responsibilities:

• Support the Data Management team where needed; analyze, contribute to and improve business processes and workflows; help with incident resolution and production data.
• Develop technical and operational subject matter expertise with Bloomberg Data Management Services which contains price, security, portfolio, and entity information.
• Understand how the data ecosystem is structured and how data is integrated and transformed across different platforms. Look for potential inconsistencies and partner with internal teams for resolution.
• Ensure portfolio, fund and entity data is available, reliable, consistent; closely monitor data quality and identify new controls to mitigate operational risk.
• Help with ongoing business initiatives; ensures alignment between stakeholders about business needs and data needs.
• When presented with problems or reporting needs, identify data and analytical process necessary to address them.
• Carry out ad-hoc data analysis to address incoming requests or out of self-initiated curiosity.
• Track and report market data usage; perform deep level analysis on market data footprints and uncover opportunities for optimizing and rationalizing data usage to reduce costs.
• Ability to generate impactful, actionable insights based on iterative data analysis, translate data-driven output into business language, and make appropriate recommendations.
• Structure large data sets to find usable information; craft complex and comprehensive SQL queries/views utilizing data infrastructure and external data sources.
• Builds reports and visualizations to effectively communicate data driven insights to a variety of audiences; e.g. graphics, dashboards to illustrate facts, trends.
• Contribute to the development and execution of the division’s data strategy that enables the most effective use of the data assets.
• Collaborate with all groups within the Chief Data Office to ensure alignment across data functions and on target delivery of the group’s goals.
• Bring forward new ideas/thought leadership/challenges to the status quo without a specific business request.
• Continuously enhance knowledge within area of expertise and keep current with industry trends and business direction.

Qualifications:

• Undergraduate degree in business, finance, computer science or a quantitative discipline
• Prior experience in the asset management / financial industry is required.
• Good knowledge of financial instruments and investment data overall is a nice to have.
• Experience in data ETL, data modeling, database and query language (e.g., SQL, PL/SQL), programming languages (e.g., C#, VB, Python) and subsequent data visualization (e.g., Power BI)
• Must possess expert level of Microsoft Excel; substantial experience writing SQL relational and multidimensional database queries and slicing/dicing data models is required.
• Strong knowledge and experience in analyzing as well as designing data models that meet reporting needs.
• Strong proficiency in data analysis, with broad understanding of data foundationally and technically, drivers of data demand and how data is used.
• Hands-on experience creating solutions leveraging a variety of technologies.
• Experience bridging the business and technology worlds with the ability to effectively articulate technical vision, possibilities, and outcomes and collaborate across teams to achieve desired results with positive attitude and strong work ethic
• Champion of change and managing the implementation of new ideas / improvements.
• Ability to work across multi-disciplinary teams to translate business needs into testable hypotheses and then design experiments to test said hypotheses.
• Execute with speed and precision, relishing all challenges as opportunities to dig in; constantly learn from both success and failure.
• Exceptional attention to detail and accuracy with demonstrated strong logical and technical problem-solving skills
• Must be a self-starter with excellent listening, communication, and organizational skills.
• Able to work well with limited supervision, tackle new problems with limited support, and require limited guidance to link each step.