About Engineering At Fonoa
Engineering is central to how we build the business. We focus on solving real customer problems, not just shipping features. We work in small, high-trust teams, with full ownership across the lifecycle, from design to production and beyond.
We value:
- Ownership – engineers own what they build end-to-end
- Simplicity – we break down complex problems into clear solutions
- Thinking Big - we innovate with modern technologies (LLMs, AI Agents, etc.) to solve meaningful problems
- Quality – scalable, maintainable systems are non-negotiable
- Speed & iteration – we ship fast, learn, and improve continuously
- Collaboration – we win as a team, not as individuals
Position Overview
As Fonoa continues to scale globally, we are looking for a Data Engineer to help us bootstrap an internal data platform from the ground up. Working closely with the Infrastructure team, you will play a foundational role in unlocking the value hidden across our different systems and make data accessible to both technical and non-technical stakeholders in a seamless way.
This is not a role where you'll walk into a mature data stack with well-defined requirements. We have data spread across multiple systems and services, and we need someone who can help us experiment, iterate, and discover how to best add value to the business and our products through data. You'll be exploring what's possible, building the first versions of pipelines and tooling, and shaping the direction of data engineering at Fonoa.
This is a highly collaborative, hands-on role for someone who thrives in ambiguity, brings curiosity and proactivity, and is energised by the challenge of building something from scratch rather than maintaining something that already exists.
What Will You Do
Data Platform Foundations
- Design, build, and iterate on the foundational data infrastructure that will become Fonoa's internal data platform
- Evaluate and select appropriate tools and technologies based on our needs, scale, and team capabilities
- Establish patterns and best practices for data modelling, cataloguing, and documentation, and data governance and quality standards early on
Data Integration & Access
- Connect and consolidate data from multiple internal systems and services into a unified, queryable layer
- Build interfaces and tooling that enable non-technical users (including Product, Finance, Operations) to consume data independently
- Ensure data quality, consistency, and reliability across sources through validation and monitoring
Exploration & Value Discovery
- Partner with stakeholders across the business to understand their data needs and identify high-impact opportunities
- Prototype dashboards, reports, and data products to test hypotheses about where data can drive decisions
- Help define what "data-driven" looks like at Fonoa and shape the roadmap accordingly
Collaboration & Enablement
- Work closely with the Infrastructure team on cloud resources, networking, and security considerations for data workloads
- Partner with Finance, Operations, and Product to understand their reporting and analytical needs, and translate those into reliable, queryable data products
- Share knowledge and advocate for data engineering best practices across the organisation
What We Are Looking For
- 5+ years of experience in a Data Engineering or similar role (Analytics Engineering, Backend Engineering with heavy data work)
- Solid proficiency in SQL and at least one programming language (Python, Scala, or similar)
- Experience building and maintaining ETL/ELT pipelines and working with data warehousing or lakehouse architectures
- Hands-on experience with modern data stack tooling — particularly a columnar warehouse (BigQuery, Snowflake, or similar), a transformation layer (dbt or equivalent), and an ingestion/CDC tool (Airbyte, Fivetran, or similar)
- Familiarity with cloud platforms (GCP preferred) and their data services
- Experience with workflow orchestration tools
- Strong communication skills and the ability to work with non-technical stakeholders and translate their needs into data solutions
- Comfort with ambiguity and an ability to make pragmatic decisions with incomplete information
- Prior experience bootstrapping a data platform or data function from scratch
Bonus Points If You Have
- Experience with data visualisation and BI tools (Metabase, Looker, Superset, or similar)
- Background working in a product-oriented SaaS company
- Experience working with financial or operational data (revenue, billing, reconciliation)
- Exposure to infrastructure-as-code and DevOps practices
What Success Looks Like
- A first iteration of an internal data platform is running, consolidating data from key systems into a reliable, queryable layer
- Non-technical stakeholders can access and explore data relevant to their work without needing engineering support for every question
- Clear patterns and documentation exist for how data is ingested, tr