Data Architect at RSM leading AI - driven data migration initiatives within Salesforce ecosystem. Implementing data governance and optimizing performance across complex datasets.
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.
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