Senior Data Engineer - Databricks Specialist at University of Otago

Dunedin, Otago, New Zealand -

Full Time

Start Date

Immediate

Expiry Date

13 Aug, 26

Salary

0.0

Posted On

15 May, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Databricks, Apache Spark, SQL, Python, Azure, AWS, Data Modelling, Data Quality, Data Governance, Performance Optimisation, Troubleshooting, Mentoring

Industry

Research Services

Description

__________ INFORMATION SYSTEMS | TE POUTOKO TUKUTUKU DIGITAL DIVISION | RATONGA MATIHIKO Who we are | Ko wai mātou At the University of Otago, our Digital Services Division is driving an ambitious transformation—building a modern, enterprise-scale data ecosystem that supports world-class teaching, research, and decision-making. We’re collaborative, forward-thinking, and focused on doing meaningful work that has real impact across the University. The role | Te mahi We’re looking for a Senior Data Engineer with Databricks expertise to help shape and deliver our modern data platform. In this hands-on role, you’ll design and build high-quality data pipelines, ensuring they are scalable, reliable, and cost-effective within a cloud-first environment. You’ll tackle complex data challenges - diagnosing issues, improving performance, and contributing to the ongoing evolution of our platform, standards, and ways of working. You’ll be working across a broad, enterprise landscape but day to day, you’ll be part of a small, collaborative team where your voice is heard and your expertise is valued. As a senior engineer, you’ll also help lift capability across the team; mentoring and upskilling others as they grow their experience with Databricks. What makes this opportunity stand out is the balance. You’ll be solving meaningful, technically interesting problems in a sophisticated environment all while working in a culture that is supportive, flexible, and genuinely people focused. • Work from home up to 3 days a week. • Enjoy a small-team feel within a larger organisation. • Step out during the day - grab a coffee at a nearby café, walk the local sports grounds, or make use of the university gym just a short walk away. This is a role where you can do great engineering work without compromising on the things that matter outside of it. What you’ll bring | Kā pūkeka me kā wheako • Deep hands-on expertise with Databricks and Spark-based platforms. • Strong capability in SQL and Python (or similar data engineering languages). • Experience working in cloud data environments (Azure preferred, AWS also valued). • A track record of building and improving data solutions at scale. • Practical knowledge of data modelling, data quality, and governance. • Strength in problem-solving, optimisation, and troubleshooting. • A collaborative, pragmatic approach and confidence working across technical and business teams. Further details | Pūroko This is a permanent, full-time (37.5 hours per week) position based in Ōtepoti, Dunedin. We offer a salary aligned with current market rates, reflective of the skills and experience you bring, five weeks’ annual leave, and a 6.75%25 superannuation scheme - alongside a culture that truly supports balance and wellbeing. You must have the right to live and work in New Zealand to be considered for this position. For further information, or to discuss the role in confidence – please contact Damian Wheeler via the contact details below. Application | Tono To submit your application (including CV and cover letter) please click the apply button. Applications quoting reference number 2600797 close on Monday, 1 June 2026. Applications may be reviewed as they are received, the University reserves the right to close this vacancy at any time. Additional Information Contact: Damian Wheeler Position details: Job Description Further Information: Department Website Create an email with a link to this vacancy: Create email Location: About Dunedin

Responsibilities

Design and build scalable, reliable, and cost-effective data pipelines within a cloud-first environment. Diagnose complex data challenges and mentor team members to lift overall Databricks capability.