Join our Corporate Investment Bank Information Architecture team as an Information Architect, specializing in Data Management and Governance.

As a Senior Lead Architect at JPMorgan Chase within the Corporate Investment Bank Information Architecture team, you will have the opportunity to progress your career in Information Architecture. You will play a crucial role in delivering, maintaining, and promoting the adoption of standards, components, processes, and tools that support the broader function of Data Management. Additionally, you will have the opportunity to collaborate with Corporate Investment Bank Technology, Global Technology, and other Lines of Business to develop contemporary and innovative engineering methods.

Job Responsibilities

Create and maintain accurate, complete, and consistent governed Data Models with lineage and metadata to facilitate traceability to other metadata classification such as Data Concepts. Collaborate to create, maintain, govern, and use Controlled Vocabularies and Data Sourcing Contracts with Business Partners and the Chief Data Office. Engage with Software Development teams to determine their requirements for Model Engineering solutions and build prototypes and pilots to explore enhancements and new ways of working. Actively engage with development teams to use, develop, and improve Information Architecture standards, tools, and processes as part of the development process. Incrementally standardize and simplify the Data and Information Architecture through the rationalization and reduction of models, interfaces, and databases. Deliver to firm-wide, CIB, and sub-LOBs Data Policy and Standards (such as the System of Record and Authoritative Data Sourcing registration and JPMC Business Data Taxonomy). Contribute towards the incremental delivery of the Data Strategy by incrementally moving applications towards strategic data sourcing and standardization of metadata and tooling. Standardize and innovate Operational and Analytical Data Management in accordance with Reference Architectures and Operating Models.

Required qualifications, capabilities and skills

Formal training or certification on Software Engineering concepts and proficient advanced experience Software Engineering experience in a data-centric role with exposure to how data is stored, moved, governed, and/or validated/cleaned using Programming Languages such as Java, JavaScript, or Python. Experience in Data Modelling and Architecture tools such as ERwin, Power Designer, and/or Magic Draw or wider use of UML tools or IDEs that integrate modeling tools such as Eclipse. An interest in data and an appreciation of the value it can bring to an organization if properly understood, organized, and governed. Knowledge of some Data Management technologies such as Relational and Columnar Databases, and/or Data Integration (ETL) or API development. Knowledge of some Data Formats such as JSON, XML, and binary formats such as Avro or Google Protocol Buffers. Experience collaborating with business and technical teams to understand, translate, review, and playback requirements and collaborate to develop Model Engineering solutions. Exposure to working with data sets through tools like SQL, JavaScript, or Python. Experience communicating unambiguously through multiple channels such as Presentations, Word Documents, workshops, and meetings.

Preferred qualifications, capabilities and skills

Knowledge of Programming Language Design and Implementation. Knowledge of core Information Architecture capabilities such as Reference Data Management, Data Quality, Metadata Management, Data Taxonomy and Classification, and Security and Protection. Knowledge of Financial Services, specifically Wholesale and Investment Banking. Experience in other Data Standards (preferably Financial) such as SWIFT, FIX, or FpML. TOGAF and/or DAMA certification. Knowledge and experience of Software Engineering Methodologies (e.g., Agile).

Capabilities

A willingness to bring an outside perspective to the team, ask questions, and learn from the breadth and depth of experience of the current IA team. A “growth mindset,” willing to take on challenges. The flexibility to switch between concurrent project assignments. An ability to envisage and compare potential solutions prior to implementation