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Barclays · 1 month ago

Data Architect/Data Design - AVP

Whippany, NJ

Full-time

Onsite

Mid, Senior Level

$105K/yr - $150K/yr

Barclays is redefining the future of banking and seeking a Data Architect/Data Design - AVP to design and maintain enterprise data models. The role involves collaborating across teams to strengthen data governance and optimize architecture for smarter business outcomes through trusted data.

BankingConsumerFinancial ServicesWholesale

H1B Sponsor Likely

Responsibilities

Analysis and documentation of business requirements to translate them into data models aligned with organisational goals

Development and maintenance of data dictionaries and glossaries to define data elements and their usage

Analysis and monitoring of data usage patterns to identify opportunities for data optimisation and improvement, in partnership with the Data Base Administrator

Strategic architecture definition and product selection

Production of logical designs in relevant subject area (technical, data, operational), showing for example: processes, objects, data flows, inputs, stored data and outputs. Identifying common components

Implementation of architectures and Identification, ownership and resolution of design related issues

Definition and documentation of data architectures standards, principles and strategies

To advise and influence decision making, contribute to policy development and take responsibility for operational effectiveness. Collaborate closely with other functions/ business divisions

Lead a team performing complex tasks, using well developed professional knowledge and skills to deliver on work that impacts the whole business function. Set objectives and coach employees in pursuit of those objectives, appraisal of performance relative to objectives and determination of reward outcomes

If the position has leadership responsibilities, People Leaders are expected to demonstrate a clear set of leadership behaviours to create an environment for colleagues to thrive and deliver to a consistently excellent standard. The four LEAD behaviours are: L – Listen and be authentic, E – Energise and inspire, A – Align across the enterprise, D – Develop others

OR for an individual contributor, they will lead collaborative assignments and guide team members through structured assignments, identify the need for the inclusion of other areas of specialisation to complete assignments. They will identify new directions for assignments and/ or projects, identifying a combination of cross functional methodologies or practices to meet required outcomes

Consult on complex issues; providing advice to People Leaders to support the resolution of escalated issues

Identify ways to mitigate risk and developing new policies/procedures in support of the control and governance agenda

Take ownership for managing risk and strengthening controls in relation to the work done

Perform work that is closely related to that of other areas, which requires understanding of how areas coordinate and contribute to the achievement of the objectives of the organisation sub-function

Collaborate with other areas of work, for business aligned support areas to keep up to speed with business activity and the business strategy

Engage in complex analysis of data from multiple sources of information, internal and external sources such as procedures and practises (in other areas, teams, companies, etc).to solve problems creatively and effectively

Communicate complex information. 'Complex' information could include sensitive information or information that is difficult to communicate because of its content or its audience

Influence or convince stakeholders to achieve outcomes

Qualification

Data modellingData architectureAdvanced SQLETL toolsData warehousingCloud-native solutionsData governanceBusiness acumenCollaborationStrategic thinking

Required

Data modelling and data architecture, preferably within financial services or large corporates

Data modelling tools such as Erwin, ER/Studio, or PowerDesigner

Conceptual, logical, and physical modelling techniques applied at scale

Advanced SQL (DDL, DML, performance tuning) and scripting languages such as PL/SQL, T-SQL, Python, or Shell

Data warehousing approaches (Inmon, Kimball) and ETL tools such as Informatica