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

• Exposure to Databricks Genie or AI-assisted development workflows
• 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