Data Architect, Paya Lebar

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.