We are working with a highly backed AI SaaS company entering a rapid scale-up phase. Their platform sits at the intersection of forecasting, automation, and agentic AI, and data infrastructure is the backbone of it all.
This is a critical technical hire as you will take ownership of a large, complex Databricks environment. You will rebuild the entire eco-system to enable scale with confidence, speed, and cost discipline. This is not a maintenance role, it is a platform reset.
You will own DataOps and MLOps across AWS and Databricks, acting as both hands‑on engineer and architectural decision‑maker. The first part of this role is the responsibility of a full overhaul of the entire Databricks platform, while working closely with the founders, data science, and AI team.
This role is for someone who thrives in fast‑paced scale‑up environments and is comfortable pushing boundaries.
The Role:
* You will lead a complete Databricks environment revamp
* Migrating off a legacy metastore to Unity Catalog
* Re‑architect orchestration using Databricks tooling such as Lakeflow Jobs and Flows
* Improving cost efficiency and observability across large datasets at multi‑billion record scale
* Establishing strong data governance foundations, including GDPR‑aware handling of PII
* Designing clean, scalable dimensional models suited to analytics and ML workloads
* This work directly impacts platform reliability, ML velocity, and company margins.
Responsibilities
* Own DataOps and MLOps across AWS and Databricks
* Design, build, and operate reliable ingestion and orchestration pipelines across batch, streaming, and CDC workloads
* Own the MLOps platform, currently SageMaker and Step Functions, with the mandate to evolve or migrate to Databricks MLflow where it makes sense
* Set standards for data quality, lineage, testing, and observability
* Drive cost efficiency and FinOps discipline across the data platform
* Partner closely with data science, AI, and security to ensure the platform is trusted, compliant, and scalable
* Make clear architectural decisions and be accountable for outcomes
Required Experience
* Deep hands‑on experience with Databricks in production environments
* Strong experience with Unity Catalog and modern Databricks orchestration patterns
* Solid understanding of data governance concepts, especially around PII and compliance
* Experience designing dimensional models for analytics and Machine Learning
* Strong AWS fundamentals (Bedrock, Sagemaker, Step Functions EC2)
* Comfortable working with dbt, PySpark & PostgreSQL
* Experience operating data platforms at scale
Nice to Have
* Familiarity with agentic systems, LLM tooling, or AI copilots
NB:
* Applicants must be located in Sydney
* Must have Australian Permanent Residency/Citizenship
Package
* Base salary in the range of $170k to $180k plus super
* Strong equity through ESOP
* Performance‑based incentives tied to platform outcomes and impact
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