Top Data Engineer Jobs in New Zealand

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.

Design, build, and optimise scalable data pipelines, platforms, and products. Ensure data quality, performance, security, and governance. Collaborate with stakeholders, analytics and AI teams, mentor junior engineers, and contribute to strategic data initiatives and continuous improvement.

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.

Design, build, monitor and optimise scalable data pipelines and cloud data solutions using Azure and Snowflake. Improve data reliability, accessibility and performance, write and optimise SQL queries, collaborate with stakeholders, and support business reporting and data-driven solutions.

Design and build ELT pipelines, semantic layers, and data models using cloud data platforms (ideally Azure Databricks). Support production fixes, follow CI/CD and team best practices, and prioritise safety, quality, and continuous improvement.

Design, build and maintain scalable data pipelines, platforms and reporting to support Simply Energy business outcomes. Translate stakeholder needs into technical solutions, modernise data architecture, enable trusted analytics, and collaborate across technology and business teams in a small, autonomous environment.

Design, build, and maintain scalable ETL/ELT pipelines and data models in Snowflake. Ensure data quality, security, and performance; document architectures; collaborate with analysts and data scientists; troubleshoot and improve data infrastructure.

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 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.

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.

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.

Design, build and maintain scalable data ETL pipelines on Databricks and Azure; develop Power BI/Databricks dashboards; manage Azure application infrastructure (IaaS/PaaS/SaaS); enforce security and compliance; automate workflows and collaborate with data engineers, analysts, and architects to support business insights.

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

Operate and maintain ETL/ELT pipelines across Azure SQL and Microsoft Fabric, monitor performance, troubleshoot failures and data-quality issues, implement monitoring/alerting, improve data flow visibility and lineage, document runbooks, validate/reconcile data, and support downstream Power BI reporting and AI services.

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 high-performance, fault-tolerant distributed backend services and data pipelines for large genomic and health datasets. Design storage, indexing, and async event workflows; optimize latency, throughput, and reliability. Partner with bioinformatics and product teams to validate scientific accuracy and guard AI-driven responses.

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.

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.

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.

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.