Hybrid Data Engineer

at Datacom

• Job title: Data Engineer
• Job type: Full Time
• Experience level: Mid level, Senior
• Salary: Not specified
• Degree requirement: Bachelor's Degree
• Tech skills: Cloud, Python, SQL
• Location requirements: Hybrid, Auckland, New Zealand

About the role

• Data Engineer building and maintaining data pipelines and datasets for Smartly, a FinTech company facilitating payroll for Kiwi businesses.

Responsibilities

• Build, maintain, and enhance data pipelines using SQL and Python to ingest, transform, and serve data from core product systems and third-party sources.
• Develop and maintain well-modelled analytical datasets to support reporting, dashboards, experimentation, and data science use cases.
• Work closely with analysts and data scientists to understand data requirements and translate them into reliable, reusable data assets.
• Monitor data pipelines and datasets, identifying and resolving data quality or reliability issues.
• Contribute to data documentation, testing, and basic observability to improve trust and usability of data across the business.
• Support the ongoing improvement of Smartly’s data tooling, pipelines, and development practices.
• Operate comfortably within a cloud-based environment, with a working understanding of how data systems run in the cloud.

Requirements

• 3+ years’ experience in a data engineering, analytics engineering, or similar role.
• Strong SQL skills, with experience writing complex and performant queries.
• Strong Python skills, particularly for data transformation, automation, and pipeline development.
• Experience using Git or similar version control systems in a team environment.
• Familiarity with CI/CD practices, including automated testing and deployment workflows.
• Working knowledge of cloud infrastructure concepts (e.g. compute, storage, permissions), ideally within a modern cloud platform.
• Experience working with relational databases and analytical data stores.
• Familiarity with data modelling concepts for analytics (e.g. fact and dimension tables).
• Comfortable working hands-on and owning data tasks end-to-end with appropriate support.
• Able to collaborate effectively with analysts, data scientists, and software engineers.
• Curious and proactive, with a desire to improve data quality and engineering practices.
• Clear communicator, able to explain data issues and solutions to both technical and non-technical stakeholders.