Senior Data Architect with a strong background in Finance or Insurance industries to lead our client in the development technical solution focused on new and enhanced data structures.

S.i. Systems
Greater London, England
15 months ago
Applications closed

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The Senior Data Architect will provide data architecture expertise on key business and IS (Information Services) initiatives. This includes database logical and physical design, BI design, design and overview of data.

Skills required to succeed in this role:

10+ years’ experience as data architect or similar role, preferably with 3+ years in financial services industry Minimum 5 years’ experience with databases - Snowflake, Oracle etc. Minimum 5 years’ experience with OLTP and OLAP/Data Warehouses. Expert knowledge of relational and multi-dimensional data models, SQL, performance tuning. 2+ years’ experience in Data Integration Tools - Talend (preferred), CDC. 2+ years’ experience in BI and data visualization tools (e.g.: PowerBI, Tableau, HighCharts). Good knowledge of cloud (AWS) and on-premise infrastructure.

The key responsibilities of the Data Architect will include but are not limited to:

Lead research, impact analysis, technology evaluations/selections, initial proof of concept, and the roll out of new tools and documented standards to IS development teams and functional areas. This includes enterprise data architecture, operational and database design, BI technology and processes; Govern definition, design, storage, maintenance, movement, and access of data; Act as a liaison between the technical and business user communities for data requirements; Participate in Enterprise architecture efforts, and ensure that standards align with Enterprise Architecture blueprints and guidelines; Perform the primary data architect/designer role on client applications in partnership with development teams; Develop and maintain data architecture, including development of data models and other data structures definitions, defining standards, promoting conformity and compatibility, and reducing redundancies in data.

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