Top Data Engineer Jobs in New Zealand

Design, build, and optimise scalable data pipelines, products, architectures, and platforms. Ensure data reliability, security, quality, governance, performance, and accessibility while translating stakeholder needs into technical solutions. Collaborate with analytics, reporting, AI, and digital teams; contribute to strategic data initiatives; and mentor junior engineers.

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.

Design, build, and maintain reliable AWS data pipelines, orchestration, and warehouse models supporting analytics, BI, and customer reporting. Resolve data issues at their root, establish testing, monitoring, and documentation practices, and use AI where helpful. Mentor junior engineers and collaborate with Product, Engineering, Customer, and Finance teams. The role also involves data quality, event-driven integrations, security, governance, and multi-region systems.

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

Leads the design and delivery of scalable customer data pipelines, products, models, and platforms using Databricks, Snowflake, and Azure. Provides data architecture guidance, implements governance, security, observability, and data quality practices, and supports Infrastructure as Code and CI/CD automation. Partners with stakeholders across customer, loyalty, CRM, and MarTech initiatives, influences technical direction, and mentors data engineers across Australia and New Zealand.

Lead enterprise data engineering initiatives, designing scalable pipelines, data warehouses, integrations, and cloud data platforms. Translate business objectives into secure, high-quality data solutions while overseeing architecture, modelling, optimisation, governance, and master data management. Collaborate with data scientists and AI engineers to operationalise analytics and machine learning, mentor team members, and promote technical excellence and continuous improvement.

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

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.

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.

Design, build, and optimise scalable batch and streaming data pipelines; develop data models with dbt; manage Snowflake and BigQuery environments; ensure data governance, observability, security, and cross-team delivery; participate in a light on-call rota.

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.

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.

Senior hands-on consulting role to design, build and optimise Microsoft Fabric-based data platforms. Build Lakehouse/Warehouse solutions, develop ingestion and transformation pipelines with Python/PySpark/SQL, apply DevOps and data governance, work directly with clients, lead technical discussions, mentor engineers, and ensure production reliability and performance.

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 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.