Data Engineering

• Design and own modular, reusable ingestion and orchestration across API, SQL stored procedures and ETL/ELT for a domain, ensuring reliable flow into the SSOT from sources such as CAMs, ERP, OT and IoT systems and SharePoint.

• Design coherent data models and feature pipelines that support reporting, analytics and machine learning.

• Own the operational health of the domain, including monitoring, alerting and incident resolution, with advanced SQL and performance tuning.

Machine Learning

• Develop and evaluate machine learning models and algorithms such as forecasting, anomaly detection and neural networks, using established libraries and managing overfitting and drift.

• Design data structures and feature pipelines that make data applicable for machine learning, and integrate models into data workflows.

• Implement MLOps for own use cases, covering data and model versioning, CI/CD, monitoring and retraining.

Governance and Ownership

• Own whole data products from end to end for a domain and operate with minimal supervision.

• Make scalable architectural decisions and fill gaps independently, confirming assumptions on larger decisions.

• Apply data governance, security and IT standards, and maintain structured change management including approvals and rollback.

Platform Ownership

• Own AVEVA PI and CAMs for a site or domain, designing Asset Framework element templates and hierarchies and developing calculations within CAMs.

• Oversee asset onboarding and offboarding across the lifecycle, ensuring accurate registration, configuration and removal, and aligning asset data with reporting frameworks and dashboards.

• Configure notifications and event frames, maintain PI interfaces and the integration into the SSOT, and resolve discrepancies to uphold data integrity.

Team Dynamics

• Explain technical choices and their implications to stakeholders outside the technical team.

• Coordinate across divisions for a domain and enforce agreed standards and timelines.

• Mentor associates and review their work.

• Introduce new skills and use cases to the team to encourage collaborative learning.

• Strong Python and advanced SQL, including performance tuning and schema and index design, with C# an advantage.

• Proven design of ingestion frameworks, integrations and data models for a domain, with scalable architectural decision making.

• Solid machine learning foundations plus practical MLOps, and the ability to develop and evaluate models and algorithms using established libraries.

• Working ownership of AVEVA PI and CAMs, including Asset Framework modelling, interfaces, alarms and event frames.

• Works independently, with strong engineering discipline and the ability to explain technical choices to a general audience.

Minimum Requirements

• Degree in Computer Science, Information Systems, Engineering, Mathematics or a related field.

• 5 to 7 years in data engineering or data platform development at a mid level, with 5 or more years accepted where there is clear evidence of independent delivery ownership.

• Hands on responsibility for API integration, ETL/ELT orchestration, data modelling and production operations.

• Strong SQL and Python, with experience in ETL tooling and automation frameworks, and practical experience developing machine learning models and integrating them into workflows.

Advantageous

• Hands on AVEVA PI configuration and integration, such as PI Integrator or APIs.

• Machine learning models in production and associated MLOps tooling, with deep learning exposure such as TensorFlow and PyTorch.

• Data lakes and big data architectures, and orchestration tools such as Airflow, Prefect or Azure Data Factory.

• Power Apps and Power Automate, and energy sector or industrial and IoT data experience.