Key Responsibilities
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-prem 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 optimize production pipelines.
2. Embedded Architecture Ownership(End-to-End)
- Own and evolve the end-to-end data architecture, from OT source systems to analytics consumption.
- Define and standardize:
i. OPC UA connectivity and subscription patterns
ii. Streaming vs batch ingestion strategies
iii. Buffering, retry, and fault-tolerance mechanisms
- Establish architectural standards for:
i. Time-series schemas
ii. Asset and tag hierarchies
iii. Naming conventions and metadata structures
- Own non-functional requirements across the platform:
i. Availability and resilience
ii. Latencyand performance
iii. Scalability
iv. Securityat the OT / IT boundary
- Act as the final technical authority for 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, enabling consistent KPI usage.
- Establish standards for:
i. Metriccalculation logic
ii. Grain,time windows, and aggregation rules
- Ensure a single source of truth for business metrics and prevent metric duplication.
- Enable self-service analytics for data analysts through well-documented, trusted data sets.
4. Data Governance, Quality &Lineage (Embedded)
- Implement data governance embedded into pipelines and analytics models, including:
i. Dataownership and domain attribution
ii. Technicalmetadata 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, explain ability, and trust
- Work closely with data analysts and stakeholders to ensure data is fit-for-purpose.
5. Collaboration & Enablement
- Partner with data analysts to translate business requirements into scalable analytics solutions.
- Validate analytics outputs against business intent and operational reality.
- Act as a technical advisor to stakeholders on data usage, limitations, 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.
