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.

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