Data Engineer

Spencer Richardson is working with a technology company developing cutting-edge AI-powered safety solutions designed to prevent collisions between people and moving machinery and improve industrial safety outcomes. Their products include human form recognition systems, real-time reporting dashboards, and analytics platforms — empowers customers across construction, mining, waste management, infrastructure and more with actionable insights and enhanced safety operations.

They are seeking a skilled Data Engineer to join their fast-growing team. You will play a key role in building, optimizing and maintaining the data infrastructure that powers their safety insights, AI models and analytics platforms (e.g., the Safety Shield Vue data reporting portal). You will work closely with software engineers, data scientists, product managers and stakeholders to turn raw data into reliable, scalable and trustworthy information products that support real-time insights and machine learning applications.

• Design, build and support scalable, high-performance data pipelines (ETL/ELT) to ingest, clean, enrich and transform data from multiple sources (IoT devices, sensors, logs, databases, cloud services).
• Develop and maintain data models, schemas and storage solutions that support reporting, analytics and AI systems.
• Ensure data quality, correctness and governance (including lineage, security, compliance and documentation).
• Collaborate with data scientists to prepare datasets for machine learning training and evaluation.
• Optimize data workflows for performance, reliability and cost-effectiveness.
• Implement monitoring, alerting and observability for data processing systems.
• Support delivery of data products used in real-time safety dashboards and analytics experiences.
• Provide technical guidance and best practices for data engineering across teams.

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Software Engineering, Mathematics, or related field.
• Proven experience (typically 3+ years) in data engineering or related roles.
• Solid experience building ETL/ELT pipelines using modern data engineering tools and frameworks (e.g., Python, SQL, Airflow, Spark).
• Experience with cloud data platforms (e.g., AWS, GCP or Azure) and associated data services (e.g., S3, Redshift, Big Query, Azure Data Lake).
• Strong SQL proficiency and understanding of database design principles.
• Familiarity with real-time data processing and streaming (e.g., Kafka, Pub/Sub) is a plus.
• Knowledge of data warehousing and analytics best practices.
• Excellent problem-solving skills and a data-driven mindset.

• Experience supporting AI/ML workflows and collaborating with data science teams.
• Familiarity with sensor/IoT data, geospatial data or telemetry.
• Experience with data observability and governance tooling.
• Knowledge of containerisation and orchestration technologies

If you think this is you, please reach out to  for more information.