Description & Requirements
Our Team Purpose and Standards:
· We help leaders at Spark succeed by streamlining processes, anticipating needs, and offering proactive solutions for a smooth journey to success.
About our Team:
Be part of a team of 15, 12 based in Auckland, 1 in Wellington, 1 in Christchurch and 1 in Hamilton. Our team purpose is to build valued, trusted partnerships by providing effective and efficient agile support.
The roles and responsibilities are varied -
- Supporting 3-4 leads
- Proactive calendar management liaising with key internal and external stakeholders.
- Event planning
- Travel bookings
- Managing expenses and Purchase Orders
- Other duties - delegated leave management, arranging carparks and catering as and when required.
- Provide backup support within the Support Services team
Working in a Squad opens up the opportunity to partner more with a variety of Leads, to help them achieve their goals by providing excellent support.
We need someone who is reliable, flexible and has a "can do" attitude with a willingness to help others. You need to be well organised and are able to prioritise workload. Experience in multiple diary management is key. Working at pace and being able to pivot is in your DNA and high attention to detail a must.
- Wellbeing - Comprehensive medical insurance, life and income protection. Access to wellbeing coaches, EAP and in-house Specialist Clinical support through our leading Mahi Tahi Wellness programme.
- Hybrid ways of working - for most teams at Spark this means being in the office for 4 days a week, and 1 day being flexible.
- Leave - in addition to four weeks annual leave, we offer purchased leave, enhanced parental leave support and study leave.
- Spark Credit – we provide permanent employees with $120 monthly Spark credit to use on any of our amazing products.
- Spark Share scheme – periodically we offer the opportunity to buy into our share scheme.
- Career development – access to an internal marketplace that connects employees with experiential, on the job learning across Spark.