Snowflake Data Engineer

Key Responsibilities
Design, develop, and maintain scalable data solutions on Snowflake.
Build robust ETL/ELT pipelines for structured and semi-structured data.
Develop high-quality and reusable SQL-based data transformations and engineering frameworks.
Design dimensional and analytical data models to support reporting, analytics, and AI use cases.
Build and maintain data ingestion solutions integrating Snowflake with cloud platforms, APIs, databases, files, and other enterprise data sources.
Implement automated CI/CD pipelines and modern DevOps practices for Snowflake deployments.
Work with cloud technologies across Azure and/or AWS.
Implement appropriate RBAC, security, governance, data quality, and monitoring controls.
Monitor and optimise Snowflake workloads for performance, scalability, reliability, and cost.
Troubleshoot complex data pipeline and platform issues across development and production environments.
Collaborate with architects, analysts, engineers, and business stakeholders to translate requirements into practical technical solutions.
Contribute to engineering standards, reusable patterns, technical documentation, and continuous improvement.
Proactively leverage CoCo, GitHub Copilot, and other AI-powered development tools across solution design, coding, testing, troubleshooting, code review, and documentation.
Identify opportunities to use AI and automation to reduce repetitive engineering effort and accelerate the development lifecycle.
What We’re Looking For
Strong hands-on experience with Snowflake and advanced SQL.
Solid experience designing and building ETL/ELT pipelines and cloud-based data platforms.
Strong understanding of data warehousing, data modelling, and modern data engineering principles.
Good understanding of Snowflake capabilities including databases, schemas, warehouses, stages, streams, tasks, roles, and performance optimisation.
Experience with orchestration and transformation technologies such as Azure Data Factory, dbt, Airflow, or similar tools are advantageous.
Experience with Git, CI/CD, and modern software engineering practices.
Experience with Python is advantageous.
Familiarity with infrastructure-as-code technologies.
Strong analytical, troubleshooting, and problem-solving skills.
Good communication skills and the ability to collaborate effectively with both technical and non-technical stakeholders.
An AI-enabled engineering mindset — proactively using tools such as CoCo, GitHub Copilot, and other AI capabilities to accelerate development, automate repetitive tasks, improve code quality, and deliver solutions more efficiently.
Good judgement when using AI-generated outputs, with a strong focus on security, maintainability, accuracy, and engineering quality.
What Success Looks Like
You are someone who can take ownership of a data engineering problem from design through to production.
You care about building solutions that are not only functional, but also scalable, maintainable, secure, automated, and well engineered.
You are comfortable working across the broader cloud data ecosystem and are always looking for better ways of working — whether through reusable engineering patterns, automation, or effective use of AI-assisted development tools.
Most importantly, you combine strong technical capability with a delivery-focused and continuous-improvement mindset.
Why Join Us?
You will have the opportunity to:
Work on exciting project for some of New Zealand’s best companies
Work on modern, enterprise-scale Snowflake data platform deployments.
Solve complex, real-world data engineering challenges.
Work across data engineering, cloud, analytics, governance, and AI.
Develop experience with modern engineering and AI-assisted development practices.
Work alongside experienced data engineers, architects, and technology leaders.
Contribute to reusable solutions and engineering practices that improve how we deliver data platforms.
Get the opportunity to work on a variety of different modern data tools.
If you are passionate about Snowflake, modern data engineering, and using AI to transform the way engineering teams work, we would love to hear from you.

Employment type
Full-time

Work arrangement
No