We are seeking a Data Architect to own the end-to-end data architecture for operational and analytics data within data centre environments. This role spans industrial data ingestion, data modeling, analytics enablement, and embedded data governance, ensuring OT data (e.g., OPC UA, BMS, PQMS) is transformed from raw telemetry into trusted, business-ready insights.
This is a hands-on architecture role responsible for defining standards, building critical components of the data platform, and ensuring operational reliability. The role serves as the technical lead for OT data ingestion, analytics architecture, and embedded data governance.
Make an impact by
1. OT Data Engineering & Platform Architecture
- Design, build, and operate OPC UA-based data ingestion pipelines from BMS, PQMS, PLCs, and sensors.
- Implement edge and on-premises data pipelines suitable for data centre environments.
- Manage raw and curated data layers, ensuring reliability, consistency, and performance.
- Address time-series data challenges, including sampling rates, timestamps, aggregation strategies, and late-arriving data.
- Monitor, troubleshoot, and optimise production pipelines.
2. End-to-End Architecture Ownership
- Own and evolve the end-to-end data architecture from OT source systems to analytics consumption.
- Define and standardise:
- OPC UA connectivity and subscription patterns
- Streaming versus batch ingestion strategies
- Buffering, retry, and fault-tolerance mechanisms
- Establish architectural standards for:
- Time-series schemas
- Asset and tag hierarchies
- Naming conventions and metadata structures
- Define and uphold non-functional requirements across the platform, including:
- Availability and resilience
- Latency and performance
- Scalability
- Security at the OT/IT boundary
- Provide technical leadership and guidance on data architecture and design decisions.
3. Analytics Architecture & Enablement
- Transform curated OT data into analytics-ready fact and dimension models.
- Design and maintain data marts and datasets for dashboards and reporting.
- Define and govern the analytics and semantic layer to enable consistent KPI usage.
- Establish standards for metric calculation logic, grain definition, time windows, and aggregation rules.
- Ensure a single source of truth for business metrics and minimise duplication.
- Enable self-service analytics through well-documented and trusted datasets.
4. Data Governance, Quality & Lineage
- Embed data governance into pipelines and analytics models, including:
- Clear data ownership and domain attribution
- Technical metadata capture (tags, units, frequency, source)
- Define and enforce data quality rules (completeness, validity, timeliness).
- Ensure end-to-end lineage and traceability from OT source systems to business KPIs.
- Apply access controls and data security policies aligned with OT and enterprise standards.
- Maintain documentation to support auditability and transparency.
- Collaborate with stakeholders to ensure data is fit for purpose.
5. Collaboration & Continuous Improvement
- Partner with data analysts and stakeholders to translate business requirements into scalable analytics solutions.
- Validate analytics outputs against business intent and operational context.
- Act as a technical advisor on data usage, constraints, and interpretation.
- Drive continuous improvement of the data platform and analytics ecosystem.
Required Skills & Experience
- Extensive experience in data architecture, data engineering, analytics engineering, or industrial data platforms (typically gained over multiple years of progressive responsibility).
- Strong hands-on experience with OPC UA (clients, servers, security, certificates, subscriptions).
- Experience with BMS, PQMS, SCADA, or industrial telemetry systems.
- Strong programming skills in Python and proficiency in SQL.
- Experience with streaming and messaging technologies (e.g., Kafka, MQTT, or equivalent).
- Solid understanding of time-series data modeling.
- Experience working in on-premises or data centre environments.
- Hands-on experience with data quality management, lineage and metadata management, and metric governance or semantic modeling.
- Ability to balance architecture, delivery, and operational responsibilities.
Nice to Have
- Experience with hybrid cloud and on-premises data architectures.
- Experience in energy, facilities, or data centre operations.
- Exposure to analytics or machine learning use cases on operational data.
- Experience defining enterprise KPIs or analytics standards.
