Address- Excellent spoken and written communication skills. A strong ability to communicate virtually e.g. conference calls and video conferencing.
- Min. 12+ Year of relevant experience. Bachelor's or Master's degree from reputed university with specialization in data/technology.
- Strong background working with large-scale data, data management, data quality, and data resiliency domains
- Demonstrable knowledge of Data Management tools and techniques
- Good working knowledge of relevant tools e.g. Visual Paradigm, IBM Infosphere Data Architect or CA Erwin Data Modeller
- Experience of eliciting, documenting and verifying requirements for the use of data across the organisation (Global Businesses and Global Functions) as appropriate to project scope
- Experience of working within a large, complex and geographically dispersed projects (including across time zones)
- Enthusiasm for proactively seeking, exploring, and developing use cases for new data and/or tools/wider industry trends
- Experience working across multiple geographical regions desirable
- Proven ability to work in a matrix multi-national operating structure
- Proven ability to work with senior stakeholders and business sponsors
- Experience of Financial Services and specifically within data domains / functions (e.g. Data Management, Data Architecture, Data Governance)
- Previous experience as a data analyst, solutions analyst or architect including delivery of data artefacts e.g. data models, data flow diagrams & metadata would be beneficial
- Global systems that create data, such as core banking platforms.
- Data aggregation platforms, such as data warehouses and big data platforms across a range of enterprise, private and public cloud enclaves.
- Data consumption platforms that ingest, process, store or transform and distribute data updates or outputs, including systems of insight (e.g. visualisation tooling) that generate analytics, MI or other operational reporting.
- Those systems which supply data to external parties
- Data integration tools or services at a physical or logical level.
- Data models and associated domain nomenclature consistent with standard and policy
- Data patterns for all of the above.
- Knowledge of cloud providers (GCP preferred), what technologies they provide, and the use cases they serve.
- Experience of Waterfall, DevOps and agile ways of working and how architectural data risks can be effectively governed.
