Senior Manager, Data Platform and Tools

The ideal candidate will be customer-facing, capable of articulating business value, and skilled at guiding teams through change while maintaining a collaborative environment.

Key Responsibilities:

• Act as the primary liaison for data tools and platforms, effectively communicating their business value to stakeholders.
• Lead and develop a team of engineers, fostering a strong culture of growth, mentorship, and technical excellence.
• Establish and oversee a Community of Expertise (COE) for data cataloging, data quality, and database tools.
• Manage vendor relationships, including license renewals and contract negotiations.
• Develop and implement a strategic roadmap for enterprise-wide adoption of data tools such as Snowflake, Power BI, and graph databases.
• Design training programs to enhance the organization's ability to leverage BI and data management tools effectively.
• Guide the execution of BI and data engineering initiatives, ensuring alignment with business needs.
• Provide structure and leadership to transform "doers" into effective leaders within the team.
• Overcome organizational challenges by fostering resilience, accountability, and a results-driven approach.

Required Skills & Experience:

• Proven experience in data and analytics leadership, managing enterprise-level data tools and platforms.
• Hands-on experience overseeing BI, data warehousing, and data engineering projects from conception to delivery.
• Deep understanding of data architecture, governance, and management best practices.
• Strong background in stakeholder engagement, communication, and change management.
• Experience establishing and scaling training programs and knowledge-sharing initiatives.
• Ability to work in Agile environments and lead teams using Agile methodologies.

Preferred Qualifications:

• Strong expertise in modern data analytics, including predictive modeling, machine learning, and statistical analysis.
• Experience managing and optimizing highly scalable data processing and visualization tools.
• Familiarity with data product concepts, machine learning integration, and advanced BI practices.
• Certifications in cloud technologies or data engineering.