Top Data Engineer Jobs in New Zealand

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.

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.

Design, build, and maintain scalable ETL/data pipelines and data models on Snowflake; ensure data quality and security; document architectures; collaborate with analysts and data scientists; optimise infrastructure and troubleshoot issues.

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.

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.

Design and deliver AI-ready, governed cloud data products and pipelines to enable AI agents, semantic retrieval, and analytics. Build scalable ELT/ETL, DataOps, CI/CD, IaC, observability, and governance; partner with stakeholders and mentor engineers to drive AI-enabled solutions across the airline.

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.

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

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.

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 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.