Top Data Engineer Jobs in New Zealand

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

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.

Design and develop backend APIs, services, tools, and data pipelines for engineering and operational data. Build, test, document, deploy, and maintain software; review code and architecture; define requirements; lead technical trade studies; support agile planning; collaborate with stakeholders; mentor engineers; and deputize for the Data Engineering Team Lead.

Senior Data Engineer responsible for designing, provisioning, implementing, and supporting Azure data platforms. The role covers data warehousing, data lakes, analytics, database migration, Azure PaaS SQL environments, automation, monitoring, CI/CD, and DataOps. The engineer will gather business requirements, deliver data solutions using Azure services and Fabric, support data ingestion and cleansing, and assist internal and external engineering teams. This is a three-month extendable contract available anywhere in New Zealand.

Build and maintain batch and streaming data pipelines, Airflow workflows, data models, and warehouse datasets for a crypto trading platform. Improve data quality, freshness, performance, and cost efficiency through monitoring, validation, troubleshooting, and optimization. Collaborate with engineering and business users to define datasets, metrics, and documentation. This fully remote APAC role requires strong Python and SQL skills, hands-on Kafka, Spark, and Airflow experience, crypto or Web3 data engineering experience, and clear Mandarin communication.

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 develop production data pipelines across SAP, retail, logistics, and marketing systems. Design scalable architectures, migrate legacy pipelines, model data for analytics and AI, and implement testing, observability, alerting, governance, and automation. Partner with business stakeholders to create trusted datasets, improve platform infrastructure, and mentor junior engineers. The role requires strong SQL and Python, dbt, cloud data platform experience, orchestration, production debugging, and end-to-end ownership.

Leads the design, development, deployment, and continuous improvement of scalable Azure data solutions. Builds cloud-native platforms using Spark, Databricks, Python, and SQL; establishes CI/CD, testing, automation, and DevOps practices; resolves complex technical issues; collaborates with global stakeholders; and mentors engineers while contributing to platform architecture and roadmap decisions.

Lead the design and development of cloud-based data platforms, pipelines, ETL/ELT processes, data models, warehouses, and lakes using Azure and Databricks. Build integrations, optimize performance, implement governance, monitor pipelines, resolve issues, and create Power BI reports. Collaborate with product managers and technical teams to deliver scalable, reusable data solutions and document processes.

Lead the design and development of cloud-based data platforms using Azure and Databricks. Build and optimize ETL/ELT pipelines, data models, warehouses, lakes, and integrations; implement governance, monitoring, and troubleshooting; and create Power BI reports. Collaborate with product managers and cross-functional teams to deliver scalable, high-quality data solutions, while documenting processes and applying coding and DevOps best practices.

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.

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.

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

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.