ABOUT SPRIGGY
Spriggy helps Aussie families teach their kids about money. Since 2016, over 1.3 million members have trusted us with pocket money, savings, school payments, and investing. We're a small team with a big mission — helping the next generation grow up money-smart and financially confident.
Our values: Win together · Learn by doing · Focus on what matters · Tell it as it is · Keep your promises
WHAT THIS ROLE IS FOR
You make Spriggy’s data accessible and trustworthy and help expand our data engineering capability. You build and maintain the tooling that moves data through the business, own the semantic layer that defines what our metrics mean, and work closely with teams across Spriggy to translate their questions into the data they need. This is a hands-on, technical role at the heart of how Spriggy understands itself.
WHAT YOU WILL DO
Data Tooling & Infrastructure
- Build and maintain the tools and pipelines that move data from Spriggy’s systems into our data platform.
- Keep the data platform reliable: well-tested pipelines, sensible monitoring, idempotent jobs, clean backfills, and graceful handling of schema changes.
- Improve how data is exposed to the business, making it faster and easier for teams to get what they need.
- Partner with Engineering to make sure data infrastructure fits cleanly with the rest of Spriggy’s technical setup.
- Evaluate and adopt new tooling where it genuinely improves quality, speed, or cost.
Data Modelling & Semantic Layer
- Design and build the data models that sit between raw sources and the business, following Medallion architecture (bronze/silver/gold) across our Snowflake and dbt setup.
- Own and evolve Spriggy’s semantic layer: clear, consistent definitions of the metrics and dimensions the business runs on.
- Make sure metrics are defined once, documented, and used the same way across teams.
- Enable self-service, setting teams up to answer common questions on their own without queuing for support.
- Keep documentation current so people can find what data exists, what it means, and where it came from.
Business Partnership
- Work closely with Growth, Product, Finance, and Member Experience to understand what they’re trying to decide.
- Translate business questions (often vague at first) into well-formed data work.
- Help teams ask better questions and trust the data they get back.
- Surface gaps where the data we have today can’t answer what the business needs.
Data Quality & Governance
- Maintain data quality standards: monitoring, alerts, and tests on the pipelines and models that matter.
- Support privacy and security requirements, including the Australian Privacy Act and Consumer Data Right (CDR).
- Manage access controls so the right people see the right data, and nothing more.
- Keep clear documentation of data assets, definitions, and lineage.
WHO YOU WORK WITH
Inside Spriggy: Engineering, Product, Growth, Finance, LRC, Member Experience
Outside Spriggy: Data tooling vendors and partners
HOW SUCCESS IS MEASURED
North star: the Spriggy team can confidently self-serve data. People get answers to questions themselves, without queuing for support and can trust what they find.
Everything below ladders up to that:
- Data pipelines and tooling are reliable, with uptime, freshness and quality meeting agreed standards
- Semantic layer is trusted: metrics are defined consistently and used confidently across the business
- Business questions get answered well and quickly, with the right data and the right context
- Data quality, privacy and compliance obligations are met, including the Australian Privacy Act and CDR
- Data documentation is current, so teams know what exists, what it means and where it came from
SKILLS & QUALITIES WE ARE LOOKING FOR
Core Skills
- Modern data stack tooling: dbt, Snowflake (or similar), ingestion (Airbyte, Fivetran), and orchestration (Airflow or equivalent)
- Semantic layer design, defining metrics and dimensions that hold up across teams
- Strong SQL (Postgres, Snowflake) and data modelling, with the ability to build models that are clear, performant, and maintainable
- Python for data engineering: building pipelines, transformations, and tooling
- Software engineering practices applied to data: version control, CI/CD, testing, and code review
- Medallion architecture (bronze/silver/gold), which Spriggy’s Snowflake warehouse and dbt models are built on
- Event-driven architecture and patterns for moving data as it changes
- Exposing data to non-technical users via BI tools, self-service surfaces, and well-documented data products
- Data quality, testing, and observability, catching issues before they reach users
- Working knowledge of data privacy and compliance (Australian Privacy Act, CDR)
Personal Qualities
- Hands-on and technical: you build things, not just spec them
- Curious about the business: you ask “what are you actually trying to decide?” before writing any SQL
- Collaborative: you sit close to the people whose questions you’re answering
- Rigorous about quality: the data you produce is trusted because it has earned that trust
- Pragmatic: you ship useful things and improve them, rather than chasing perfect
- You live Spriggy's values in everything you do
EXPERIENCE & QUALIFICATIONS
Required
- 5-6 years in data engineering, ideally in a startup or fast-moving tech environment
- Strong SQL and hands-on experience with the modern data stack: dbt, Snowflake, Postgres, and ingestion/orchestration tools (Airbyte, Fivetran, Airflow)
- Working proficiency in Python for building data pipelines and tooling
- Hands-on experience with Medallion architecture (bronze/silver/gold), which our Snowflake warehouse and dbt setup are built on
- Understanding of event-driven architecture and how it shapes data movement
- Experience building and maintaining semantic layers or shared data models that multiple teams rely on
- Track record of translating business questions into well-formed data work
- Comfortable working directly with non-technical stakeholders: listening, scoping, and explaining trade-offs
Preferred
- Experience in fintech, consumer technology, or regulated financial services
- Familiarity with the Australian Privacy Act and Consumer Data Right (CDR)
- Experience with BI and self-service tooling (Looker, Mode, Hex, Metabase, or similar)
- Experience with data observability and quality tooling (Monte Carlo, Elementary, or similar)
