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EatClub is Australia's #1 Food & Drink app and a 200+ person scaleup helping over 4 million customers discover restaurants and access real-time deals, while empowering 8,000+ venues to maximize revenue. The company was awarded Australia's Fastest Growing Tech Company by the AFR in 2025.
You will own the data platform—the ingestion pipelines, orchestration, governance, and cost/reliability infrastructure that every team in the business depends on. This is a genuine opportunity to build the data foundations properly for the first time, with decisions you make now remaining load-bearing in three years.
Day-to-day, you'll work on systems underneath analytics: building and operating ingestion into the bronze layer (new source integrations, CDC feeds, third-party APIs), owning orchestration workflows with real SLAs, hardening CI/CD with trunk-based development and gated deploys, governing the platform (grants, lineage, tagging, security), optimizing cost and performance, building data quality and observability, and making the platform self-serve so analysts and data scientists can ship independently.
The team operates in an AI-first culture using Claude Code via Anthropic Enterprise, agentic and async workflows, with humans on final review. You'll ship in days, not quarters, with real pipelines, dashboards, and feedback every week.
The role is not a good fit if you prefer business-facing analysis over systems work, dislike fast-paced environments with shifting priorities, are uncomfortable owning ambiguous problems end-to-end, or prefer fully-specified specs over designing solutions yourself.
**Requirements:**
- Solid experience writing and optimizing SQL for commercial-scale products, plus strong Python
- Hands-on experience building and operating production data pipelines on a cloud data platform
- Experience with orchestration and scheduling (Databricks Workflows, Airflow, Dagster, or similar), including operational troubleshooting when jobs fail
- Experience working with internal APIs and third-party tools to ingest data efficiently while maintaining strict data integrity
- Strong grasp of software engineering best practices (Git, CI/CD, trunk-based development, tests)
- Familiarity with AI-first working style: Claude Code, agentic workflows, AI in daily loop
**Nice-to-have:**
- Experience with Databricks and dbt Core
- Familiarity with AWS
- Cost and performance tuning on Spark or Databricks (partitioning, liquid clustering, warehouse sizing)
- Streaming or CDC experience (Structured Streaming, Debezium, Delta Live Tables)
- Track record implementing data quality, security, and documentation best practices
- Experience with AI/LLM experiments for practical business applications
