About the Job
Founded in Australia in 2019, our mission is to create the most efficient AI infrastructure by combining cutting-edge technology with a steadfast commitment to sustainability.
At Firmus, we are unique in our approach. We design, build, and operate a new class of digital infrastructure – the AI Factory. Through our model-to-grid technology approach, we have pushed the boundaries of multi-generational liquid cooling systems, energy management, AI software orchestration, and construction. Fo...
Our large-scale GPU cloud platform, Firmus AI Cloud, is purpose-built to deliver energy-efficient AI compute at scale to customers.
Key Responsibilities
Data Architecture & Technology Strategy
Own the data architecture across Firmus: the patterns, technology choices, and technical standards that teams build on. Make the build-versus-buy, storage-topology, and streaming-versus-batch decisions the organisation lives with long-term and own the outcomes they lead to.
Platform Build & Operations
Build the core of the data platform hands-on: the real-time and AI data pipelines, the storage layer, and the transformation and semantic layers that serve BI tooling, internal analytics, and customer-facing reporting. Collaborate with the data engineers and platform engineers to deliver and operate streaming and batch ingestion from GPU telemetry, cooling sensors, billing, and incident sources, with clear reliability, schema-evolution, and latency SLAs. Data components run as infrastructure-as-code on the self-hosted, bare-metal Kubernetes. You own their deployment, quality, and storage cost, and work with platform engineers on backup, recovery, and incident response.
Governance, Quality & Compliance
Define and enforce data contracts, lineage, and access control across teams. Set organisation-wide data quality and observability standards: schema contract enforcement, freshness SLAs, and anomaly detection. Extend Firmus' existing SOC 2 Type 2 and ISO 27001 controls to the data platform as it scales into new data centres and meet the regulatory requirements that apply to our enterprise and government customers.
Technical Leadership
Raise the standard of data engineering in Firmus. Run design and architecture reviews, drive the RFC process, and catch architectural problems before they reach production. Mentor engineers on data architecture, data quality, and operational practice, building capability rather than a dependency on you.
Stakeholder & Customer Interface
Present architecture decisions and trade-offs to the CTO and engineering leadership, direct about risks and clear about recommendations. Partner with the AI and application team so the architecture supports AI agent workflows and hardware failure prediction. Engage enterprise and government customers on architecture, lineage, or compliance when the conversation needs a senior technical voice.
Required Skills & Abilities
Bachelor's degree in computer science or a related technical field.
10+ years in data engineering or data architecture, including at least 5 years making platform-level decisions that other teams depended on. You are still hands-on and intend to stay that way.
You have been the most senior data engineer or architect in an organisation before, owning the architecture rather than contributing to it.
Deep, hands-on knowledge of end-to-end data architecture: ingestion, storage patterns, transformation, semantic layer design, observability, and governance.
Track record designing large-scale data platforms that integrate data across multiple regions or data centres.
Production experience operating high-cardinality time-series and telemetry pipelines at high ingest and update rates.
Production experience across columnar, time-series, relational, object, and vector storage patterns. You know when to use each one and what it costs to operate.
Hands-on experience with event streaming platforms such as Apache Kafka for extremely high-throughput, low-latency ingestion.
Hands-on experience with both managed data platforms (such as Databricks or Snowflake) and their self-hosted, open-source equivalents (such as Spark, Trino, ClickHouse, and lake house table formats like Delta Lake or Apache Iceberg).
Strong proficiency in Python and SQL, with distributed processing frameworks such as Apache Spark.
Experience with pipeline orchestration and transformation tooling such as Airflow and dbt, with data quality testing built into standard pipeline development.
Kubernetes experience covering Helm-based deployments and operation of stateful data workloads on self-hosted, bare-metal infrastructure.
Experience delivering data governance programmes against compliance frameworks such as SOC 2 Type 2 or ISO 27001, not just awareness of them.
Experience running architectural reviews and setting engineering standards across teams.
Willing to take part in the incident-response on-call rotation for the services your team owns.
Willing to travel overseas occasionally when the role requires it.
Clear and effective written and verbal communication in English. You can explain a complex architectural decision to a CTO or customer without losing the substance.
Background in AI infrastructure, GPU cloud, or high-performance compute, particularly working with infrastructure telemetry data at scale.
Experience with industrial IoT and OT telemetry pipelines.
Production experience with vector databases and embedding or retrieval pipelines for LLM and AI-agent use cases such as failure prediction and incident response.
