Analytics Manager

External & Internal Interfaces

External Interfaces:

• Vendors (if required)
• Contractors (if available)

Internal Interfaces:

• Global Real Estate & Facilities Management Team
• GCN REFM Team
• Other relevant teams, including Global Data & Analytics, Global Technology, and Business Units

Job Requirements

Education:

• Bachelors degree required, preferably in a quantitative field (Statistics, Business Analytics, Math, Economics, etc.)
• Masters degree preferred (MBA/MS Computer Science/M.Tech Computer Science, etc.)

Relevant Experience:

• 9+ years of experience in an analytical role, with 3-5 years of experience leading analytical teams in real estate and/or facilities maintenance analytics
• Relevant experience in advanced analytics, Power BI dashboard development

Behavioral Skills:

• Delivery Excellence
• Business Acumen
• Social Intelligence
• Innovation and Agility
• Team Building

Knowledge & Technical Skills:

• Functional Analytics (Real Estate, Property Management, Facilities, Maintenance, etc.)
• Statistical modeling using analytical tools (R, Python, KNIME, etc.)
• Big data technologies & frameworks (AWS, Azure, GCP, Hadoop, Spark, etc.)
• Enterprise reporting systems, relational (MySQL, Microsoft SQL Server) and non-relational (MongoDB, DynamoDB) database management systems, and data engineering tools
• Business intelligence & reporting (Power BI including Power Query & DAX, Tableau, Alteryx, etc.)
• Microsoft Office applications (MS Excel, etc.)

Roles & Responsibilities

Analytics & Strategy:

1. Build, lead, and train a team of analysts and senior analysts to support the REFM team.
2. Lead the design and development of reports and dashboards for the Real Estate and Facilities Management team.
3. Oversee the creation of comprehensive periodic and quarterly reports to generate actionable insights.
4. Apply multiple algorithms or architectures and recommend the best model with detailed insights to promote data-driven business decisions.
5. Utilize cloud-based data processing for statistical modeling and big data analysis, and leverage visualization tools to represent large datasets.

Operational Excellence:

1. Follow industry standards in coding solutions and adhere to programming lifecycle best practices.
2. Partner with Data Engineering teams to design, build, and maintain core data infrastructure, pipelines, and automated workflows for dashboards and analyses.

Stakeholder Engagement:

1. Work collaboratively with multiple stakeholders, including business functions, data engineers, and data visualization experts, to deliver project outcomes.
2. Communicate complex data insights to business teams using clear and innovative formats.