This role shapes and owns the trading data layer, including the data model, architecture, and integrations, leveraging a deep understanding of the business’s trading data requirements. Acts as the custodian of Trading business and data needs by proactively identifying, documenting, and interpreting requirements to build a comprehensive view of data needs. Ensures the trading data layer is consistent,scalable, and interoperable across platforms and digital solutions. This position will be based residentially in Singapore.

Trading data model ownership​

• Shape and own Woodside's Trading data model, taxonomy and trading data governance capabilities​
• Ensure that overarching architectural principles are maintained to guide technical changes towards a data-centric architecture​
• Drive compliance with Woodside Trading architecture blueprint and other Woodside architecture standards (including in relation to the Enterprise Data Platform) – and ensure alignment across domains and ecosystems ​

Trading data model design and delivery​

• Translate Trading use case needs into data requirements​
• Carry out gap assessments of current data setup vs. data requirements​
• Drive closure of gaps where they exist, e.g. by initiating selection of suitable technologies (and initiating associated processes) ​
• Capture Trading-specific knowledge from 3rd party vendors and ensure continuous upskilling and advancement of Woodside digital teams​

​Integrated view across thetrading landscape​

• Collaborate with the Trading Ecosystem Owner and Pre- and Post-Trade Domain Owners to seamlessly optimise trading technology ecosystems and maximise business value from the overall technology landscape

1. Skills based on the job descriptions：  

• Strong expertise in modern Asset-backed trading, including deep understanding of value chains for LNG and Oil trading, and knowledge of associated business processes​
• Proven ability in translating trading & risk requirements into robust data requirements​
• Proven capability in designing and implementing modern data platforms and data models for trading, and in continuously improving data models to meet business changing data requirements​
• Strong data governance, lineage, cataloguing and quality management expertise​
• Deep knowledge of pre-trade and post-trade capabilities including e.g. analytics, portfolio optimisation, price curves, market access, valuation, risk, trade capture etc.​
• Strong communication skills to bridge gap between Front-, Middle- and Back-office business requirements and technology ​
• Pragmatic approach to trade-off and decision facilitation