Data Architect

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.