Senior Data Engineer

Firmus Technologies

Firmus Technologies is a global leader pioneering the development and operation of efficient AI infrastructure across Asia Pacific.  

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. For our customers, this approach allows us to make every watt count and deliver low-cost AI tokens globally. 

Firmus AI Cloud

Our large-scale GPU cloud platform, Firmus AI Cloud, is purpose-built to deliver energy-efficient AI compute at scale to customers. 

It empowers developers, enterprises, educational institutions, and government users to train and deploy AI models with unmatched efficiency and cost savings. With an ever-growing suite of services and applications, we are committed to delivering a cloud experience that is market-leading, proprietary, and built to scale. 

ROLE

Firmus Technologies is seeking a Senior Data Engineer to join our Engineering and Technology team. You will lead the design, build and operation of self-hosted data platform across its full stack: streaming telemetry off GPU compute and cooling systems, data lake feeding hardware failure prediction models, AI-native layers powering agent-based workflows, and the governance layer that makes them secure and discoverable. This is the platform that connecting AI workload performance to energy efficiency across Firmus AI factories. 
 

KEY RESPONSIBILITIES

• Data Ingestion & Pipelines  
  Design and operate streaming and batch ingestion pipelines from GPU cluster telemetry, cooling sensors, power grid, and incident data. Own reliability, schema evolution, and latency SLOs from source to storage. 
   

• Storage & Modelling 
  Design and implement storage solutions across columnar, time-series, relational, object, and vector store patterns. Select the appropriate technology for each workload and own its operation end-to-end. 
   

• Data Quality & Observability 
  Implement data quality validation across ingestion, transformation, and serving layers, covering completeness, freshness, schema consistency, and anomaly detection. Build cross-layer observability to surface and resolve issues before they reach downstream consumers or AI models. 
   

• Transformation & Serving 
  Build the transformation layer that produces clean, well-modelled data from raw ingested data. Define the semantic layer serving BI tooling, internal analytics, and customer-facing reporting. Design vector retrieval pipelines for AI agent use cases including hardware failure prediction. 
   

• Governance & Trust 
  Implement data catalogue, lineage tracking, access control, and data contracts across teams. Ensure data is discoverable, auditable, and compliant with SOC 2 Type 2 and ISO 27001 requirements. 

  

• Platform Deployment & Operation

Deploy and operate all data platform components via infrastructure-as-code on Kubernetes, covering Helm-based deployments, CI/CD integration, secrets management, backup and restore, capacity planning, and incident response for self-hosted        infrastructure. 

SKILLS AND EXPERIENCE  

• Bachelor’s degree in computer science or a related technical field.  

• 7+ years of experience in data engineering, ideally with a focus on cloud infrastructure, including at least 3 years owning a production data platform end-to-end, with demonstrated experience operating hybrid infrastructure: self-hosted infrastructure and managed cloud services such as Snowflake, BigQuery, or Databricks. 

• Proven track record in designing and implementing large scale data platforms that integrate various components with data source from multiple regions 

• Strong proficiency in Python and SQL. 

• Hands-on experience with event streaming platforms such as Kafka for high-throughput, low-latency ingestion. 

• Production experience with both columnar and time-series databases, including query optimisation and storage tuning at scale. 

• Experience with pipeline orchestration and data transformation tooling such as Airflow and dbt, including data quality testing and validation as part of standard pipeline development. 

• Practical knowledge of data observability – monitoring data freshness, schema drift, volume anomalies, and lineage across a live data stack. 

• Kubernetes experience covering Helm-based deployments, infrastructure-as-code, and operation of stateful workloads. 

• Familiarity with vector database integration and embedding pipelines for LLM and AI agent use cases. 

• Working knowledge of data governance frameworks, access control design, and data contracts, with awareness of SOC 2 Type 2 and ISO 27001 requirements. 

• Clear and effective communication in English, both written and spoken.