Top Data Engineer Jobs in Wellington

The Azure Data Engineer role involves designing and implementing data solutions on Azure, working with multiple projects, and consulting with clients. Key responsibilities include developing data architectures, continuous integration practices, and engaging in training and continuous improvement initiatives.

Lead the design, delivery, and operation of enterprise-scale data platforms, pipelines, and products supporting analytics, business intelligence, machine learning, and AI. Shape NZTA’s modern data platform, establish engineering standards, manage technical debt, and ensure secure, reliable, governed solutions. Work across squads and programmes while coaching data engineers, influencing technical outcomes, and contributing to continuous improvement and engineering excellence.

Design, build, and optimise scalable data pipelines, products, and platforms. Implement modern data architectures, ensure data reliability, quality, security and accessibility. Collaborate with stakeholders and analytics/AI teams, mentor junior engineers, monitor performance, and drive continuous improvement and strategic data initiatives.

Develop, deploy, and maintain LLM pipelines and RAG QA/search systems; design and optimize prompts and multi-agent LLM architectures; operate multi‑GPU/cluster inference; build evaluation pipelines for model quality, bias, and hallucination; collaborate with product and CS teams to integrate conversational AI.

The Senior Data Engineer/Consultant will lead data engineering engagements, focusing on Databricks solutions, mentoring teams, and delivering high-quality data pipelines and integrations.

Build and maintain production data pipelines ingesting from SAP, POS, logistics and marketing systems. Migrate legacy pipelines to modern architectures, monitor and improve data quality, support platform infrastructure, collaborate cross-functionally, document processes, and apply automation and AI to improve workflows.

Lead design, development, deployment, and continuous improvement of cloud-native data and analytics solutions on Azure. Act as a hands-on technical lead building scalable, secure end-to-end data platforms using Spark/Databricks, Python/PySpark, and orchestration tools. Ensure CI/CD, DevOps, automation, code quality, mentor team members, resolve complex technical issues, and collaborate with stakeholders and vendors to drive platform evolution.