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Data Engineer

dataengine ‱ 🌐 In Person

In Person Posted 2 days, 21 hours ago

Job Description

The Data Engineer role is primarily focused on designing, building and operating modern cloud‑based data platforms for clients. The position centres on data engineering, DataOps and MLOps delivery across Snowflake, AWS and Azure.

A portion of the role involves software engineering activities, including the development of reusable code components, automation scripts and cloud‑native services that support data pipelines, deployment processes and operational reliability.

The role operates within a consulting environment, requiring collaboration with clients, solution architects, data architects, data engineers, analysts and data scientists to deliver scalable, production‑ready data and ML platforms.

This role is based in Auckland, and requires you to work Monday - Friday, from 8.30am - 5.00pm, a total of 40 hours per week.

Key Responsibilities:

Design, build and maintain scalable data pipelines and cloud data platforms using Snowflake across AWS and Azure.

Develop and optimise ELT/ETL workflows using modern data engineering frameworks and orchestration tools.

Implement and manage data warehouses, data lakes and lakehouse architectures to support analytics and machine learning workloads.

Apply DataOps best practices including CI/CD, automated testing, monitoring and deployment of data pipelines.

Enable MLOps workflows by delivering reliable data ingestion, feature pipelines and model‑ready datasets.

Implement infrastructure as code using tools such as Terraform or Azure Bicep to provision and manage cloud resources.

Ensure data quality, performance, security and governance standards are embedded within data platforms.

Design and develop reusable software components, scripts and services to support data pipelines, automation and platform operations.

Write, test and maintain code using appropriate programming languages (e.g. Python) following software engineering best practices.

Apply version control, code review and CI/CD practices to software components supporting data and ML platforms.

Contribute to the design and maintenance of cloud‑native services and APIs that integrate with data platforms.

Work with clients to gather requirements and translate business needs into technical data and platform solutions.

Collaborate with cross‑functional teams including solution architects, data scientists and analysts.

Provide technical guidance and recommendations aligned to cloud data engineering, DataOps and MLOps best practices.

Key Requirements:

Minimum 3 years’ professional experience in data engineering, software engineering, or cloud engineering roles with hands-on delivery of production data platforms

Bachelor’s degree in Computer Science, Software Engineering, Information Technology, or a closely related discipline

Strong experience with

Snowflake

for data warehousing and analytics.

Experience working across

AWS and/or Azure

cloud environments.

Proficiency in SQL and experience handling structured and semi‑structured data.

Experience implementing DataOps practices including CI/CD, automation and monitoring.

Exposure to MLOps concepts such as feature pipelines, model data readiness and operational ML workflows.

Software development experience using programming languages (e.g. Python), applied primarily to data and platform enablement.

Experience with Infrastructure as Code tools such as Terraform or Azure Bicep.

Strong communication skills with the ability to collaborate effectively with technical and non‑technical stakeholders.

Analytical problem‑solving skills with attention to quality, reliability, and performance.

Ability to work independently and as part of a delivery team in an enterprise or professional services environment.

Cloud certifications (AWS and/or Azure) are desirable.

Must be based in Auckland, New Zealand.

Availability to travel to customer sites as required.

Here’s a Taste of What’s on Offer at dataengine:

Opportunity to solve real-world business problems.

Collaborative and supportive work environment.

Competitive salary.

Be at the forefront of data science innovation in a dynamic startup environment.

Work on a variety of challenging and impactful projects across different industries.

Opportunity to learn and grow your skillset with the latest technologies.

Continuous learning and development opportunities.

Weekly fresh fruit, snacks, monthly industry led lunches and active staff activities.

Be part of a company that is shaping the future of data-driven solutions.

About dataengine:

dataengine, a New Zealand-based leader in data solutions, empowers businesses to unlock the hidden value within their data and achieve true transformation. We connect people, processes, and technology for optimised efficiency and sustainable impact. Founded in 2018, we've grown rapidly thanks to our exceptional team and commitment to delivering practical, impactful results. We believe in building the foundation right, starting with strategic planning and robust architecture.

What sets us apart?

Experienced \& Pragmatic:

Our team boasts extensive expertise, focusing on tangible outcomes, not just presentations.

Small Wins, Big Impact:

We deliver incremental value drops, ensuring our customers see results quickly and continuously.

Holistic Enablement:

We develop with our customers, in their environment, empowering our team to own and optimise solutions.

Leading the way in Generative AI:

We've developed a unique standalone AI engine, providing secure and accessible tools for our customers’ AI journey. From use case brainstorming to model deployment and tuning, we offer comprehensive support throughout the entire lifecycle.

Beyond AI:

Our services range from advisory and resourcing to full-scale platform design and build, catering to any data-driven business need.

Our Values

Nimble

- We move fast but think big

Quality

- We do it right the first time

Co-Creation

- We work together to solve precise problems

We are All Ears

- We listen and then do

Progressive Thinkers

- We are creators and doers

Pragmatic

– We don’t over-engineer

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