Data Architect

Singtel; Singapore; On-site; Confidential; Full time

Job summary

Singtel is seeking a Data Architect to own end-to-end data architecture for operational and analytics data within data centre environments. You will shape ingestion, modeling, analytics enablement and embedded governance across OT data streams such as OPC UA, BMS and PQMS.

This hands-on role requires defining standards, building core components of the data platform, and leading data governance across pipelines and semantic layers to enable trusted KPI reporting and self-serve analytics.

Qualifications

• Extensive experience in data architecture and engineering in industrial settings.
• Hands-on OPC UA development including clients/servers and subscriptions.
• Proficiency in Python and SQL for data processing and analytics.
• Experience with streaming technologies like Kafka or MQTT.

Responsibilities

• 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.
• 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, buffering, and metadata structures.
• Establish non-functional requirements: availability, latency, security at OT/IT boundary.
• Transform curated OT data into analytics-ready fact and dimension models; design data marts and datasets.
• Define governance, metadata, and semantic layers to ensure trusted KPI usage and self-service analytics.
• Embed data governance, data quality rules, lineage, and access controls across pipelines.

Skills

Data Architecture

OPC UA

Python

SQL

Time-series modeling

Industrial data platforms

Tools

Kafka

MQTT

BMS

SCADA

Job description

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
• Buffering, retry, and fault-tolerance mechanisms
• 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.
• 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.
• 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.