Data Platform Manager

Summary

The Data Platform Manager is critical to enabling the Data Engineering vision and strategy for the company and delivering solutions that transform how we operate and make decisions. The Manager oversees the portfolio of work supporting data platforms that serve the reporting, analytics, and data science needs of the company. This includes managing the strategy, architecture, development, enhancement, maintenance, and support of these platforms. The role is a leadership position directing a team of Data Engineers who collaborate across multiple teams within the Technology & Data Science department and with other business partners to build and deploy solutions. As a key leader on the Data Engineering team, the Data Platform Manager shapes the policies and processes guiding the planning and execution of work. Success is based on the stability of these platforms as well as on-going improvements in reducing the resources and turn-around time to deliver reporting and analytical solutions.

Key Responsibilities

Vision and Strategy

• 
  Accountable for leading the execution of the Data Platform portfolio of work.
• Set direction on the technical architecture, patterns, and practices used by the team and ensure alignment with department principles, policies, and standards.
• Prioritize work based on its long-term ability to provide value with incremental releases. Identify business outcome metrics to measure each product’s success.
• Develop a multi-quarter product roadmap that articulates a clear vision and strategy for the data platforms.
• Communicate the roadmap and vision with stakeholders, building support and advocacy for the work.

Development

• Manage the data platforms throughout their lifecycle from creation to decommissioning.
• Ensure a high-quality architecture using consistent patterns and practices.
• Proactively prioritize and manage the backlog for all products within the Data Platform portfolio.
• Manage the team’s resource allocation in accordance with the prioritized portfolio.
• Manage the capture and documentation of platform and product requirements.
• Assist the Engineers on the team with setting priorities, making decisions, and removing roadblocks.
• Oversee the execution of testing and production releases with all contributors and stakeholders.
• Setup the support process for each platform and product aligned to the overall support strategy.
• Ensure platform performance, stability, and cost are proactively managed.

Leadership

• Lead all Engineering activities for each platform and associated products.
• Plan, direct, and monitor activities of individual contributors and/or contractors.
• Understand, articulate, and optimize solutions, processes, and procedures used by the team and stakeholders.
• Develop and present analyses to management (e.g., cost/benefit, implementation plans).
• Identify budget requirements to successfully carry out the portfolio.
• Partner with Procurement to evaluate and negotiate third party solutions and services.
• Ensure team meets or enables achievement of relevant metrics and commitments.
• Demonstrate business acumen and apply it for effective business engagement and alignment with internal customers.
• Drive change management in the team and the department.

Typical Education

Bachelor's Degree (B.A. or B.S.) from 4 year college or university

Relevant Experience

8+ years related experience and/or training

Minimum Qualifications

• Experience in product management with demonstrated ability to manage all aspects of a successful product throughout its lifecycle.
• Strong technical background in data engineering, cloud services, and data platforms, like Databricks, and business intelligence platforms, like PowerBI.
• Experience with the following technologies and languages: Microsoft Azure, Databricks, Git, Terraform, and Python.
• Excellent problem-solving skills and a drive to reach resolutions acceptable to multiple stakeholders.
• Skilled at working effectively with cross-functional teams in a matrix organization.
• Excellent written and verbal communication skills with the ability to influence stakeholders
• Familiarity with AI and machine learning technologies and their application in business.
• Understanding of data management techniques, especially improving data quality.
• Travel may be periodically required (<15%) to meet with internal customers and attend conferences.