Top Data Engineer Jobs in New Zealand

Design, build and maintain scalable data pipelines and models using Databricks, Azure Data Factory and Microsoft Fabric. Optimise BI models and ETL processes, translate business requirements into actionable data solutions, lead and mentor junior engineers, and collaborate with stakeholders to deliver analytics and reporting across the business.

Design and deliver scalable, governed AI-ready data products and cloud data pipelines. Enable AI capabilities (agents, semantic retrieval), implement DataOps/CI-CD, observability, and Iac, partner with stakeholders, and mentor engineers to drive enterprise analytics, automation, and AI solutions.

As an Implementation Engineer, you'll manage ERP integrations, configure data sources, build custom reports, and enhance product delivery through customer feedback, primarily focusing on the construction supply industry.

The Data Engineer will design, build, and maintain data pipelines and platforms, ensuring data is reliable and accessible for business insights. Key tasks include optimizing SQL queries, data modeling, and collaborating with stakeholders for data governance and analytics solutions.

The Senior Data Engineer will design, build, and maintain scalable data pipelines, collaborate with teams, ensure data quality, and evolve data practices.

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.

Own and build evaluation datasets, pipelines, and tooling to measure and improve LLM-powered feature accuracy. Design ground-truth and labeling systems, run error analysis and audits, optimize prompts and models from experimentation through production, and automate large-scale eval and monitoring across AI products while collaborating with engineering, product, and inspection stakeholders.

Lead development of innovative, high-quality applications while collaborating with cross-functional teams and establishing engineering standards. Requires strong knowledge in Java and related technologies.

Build, maintain, and improve SQL/Python data pipelines and analytical datasets. Collaborate with analytics, data science, product, and engineering teams to ensure data reliability, observability, testing, and documentation in a cloud-based environment.

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.

Build, deploy, and support cloud-native data and analytics solutions on Azure. Design and maintain data pipelines, ingestion, transformation and integration processes using Apache Airflow. Ensure data quality, monitoring, CI/CD and infrastructure-as-code. Troubleshoot platform issues, collaborate with cross-functional teams, and contribute to platform improvements and documentation.

The Senior Data Engineer will develop and maintain data pipelines, create reports, ensure data accuracy, and mentor junior engineers while managing production data services.

Seeking Senior and Intermediate Data Engineers to enhance data platform capabilities, deliver technical projects, and mentor team members in a collaborative environment.

As a Quality Assurance Engineer at Foodies, you will ensure data accuracy and reliability by testing ETL/ELT pipelines, validating complex datasets, and promoting data quality in Agile delivery squads.

The Senior Data Engineer builds real-time data pipelines, implements data mesh architectures, and engages with clients to enhance data interactions, focusing on creating impactful data solutions.

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.

Lead delivery and quality of modern web experiences: define front-end architecture and design systems, implement accessible, performant, and secure UI, embed testing and observability, mentor engineers, partner with Product and Design, and improve platform and pipelines for scalability and developer experience.

The role involves data collection for model training, utilizing cloud infrastructure, collaborating on dataset roadmaps, and finding new audio data sources.

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

Lead development of domain data products, improve data governance and reliability, partner cross-functionally for analytics and ML use cases, mentor data engineers, and optimize data solutions.

The Senior Data Engineer will lead data quality assurance, manage and enhance the analytics platform, design self-service data products, deliver analytics capabilities, and mentor junior team members.

The Data Implementation Engineer will manage data integration for accesso Intelligence, ensuring data quality and system health while collaborating with customers and internal teams to enhance API and third-party system integrations.