JPMorgan Asset Management (JPMAM) continues to drive towards best in class data management capabilities to ensure all areas of the business and operations make decisions or report information based on a clear understanding of the data they are using and its quality. In parallel technology are looking to overhaul the way they deliver and document data. A model driven service architecture coupled with a comprehensive metadata solution underpins the target state data architecture.
To achieve these objectives we are looking to build out a core data architecture team by recruiting a number of experienced and junior data architects and data modelers who will report directly to the JPMAM Chief Data Architect. The core data architecture team will need to work in a collaborative manner with business sponsors and data product owners to maintain the canonical/logical and physical data models, JSON and XSD schemas.
The Data Architect/Modeller Trainee position is part of a central Data Architecture Team that is responsible for owning, developing and maintaining the JPMAWM Corporate Data Model. The role requires someone with good analytical and communication skills. The candidate should have a good technology background with some working knowledge of relational database and NoSQL database theories. The JPMAWM Corporate data model is a critical component of the technology organisation as it describes the business data requirements and data integrity rules which provides the basis for data communication, database design, data integration, data quality assessment and data documentation.
The role will involve working with senior data modellers to develop high level and detailed logical data models with an associated controlled vocabulary, both of which will need to be versioned controlled and managed in the JPMAM Metadata platform.
Instruction will be given on specific data modelling tools and technologies however a good grasp of Excel and other MS Office tools is expected. Career progression will be towards a more senior data modelling position with opportunities to develop into other roles such as data analyst, data scientist, business intelligence solutions or business analysis.
New approaches to data modelling such as RDF and the Semantic Web are now emerging. Working on the assumption that the candidate is a recent graduate there will be some opportunities to explore some R&D activities ideally with academic institutions to see how these new techniques and concepts can assist our business.
Working in isolation or with senior data modellers to capture business data requirements as a logical data model. Ensuring these models are consistent and that the Business Glossary is linked and maintained in a manner that allows impact assessment and lineage of metadata.
Data model integration. The Corporate data model is actually 40+ individual models all linked together to form a single corporate model. All the individual models need to be checked to ensure they cross reference each other accurately.
Semantic model mapping from Industry based models to JPMAM specific models to leverage industry standards and to test model completeness.
Metadata support. Providing access to metadata information though the generation of pre-prepared or dynamic queries.
Data quality analysis. Maintaining data validation rules, testing those rules and running data quality reports or generating data quality dashboards.
Current state documentation and data lineage capture. In many cases existing database will need to be reverse engineered to assist in bringing all our data dictionaries up to date. At the same time we do this we need to capture and document data lineage. This part of the job will require the candidate to become familiar with a range of different technologies to perform these functions so the ability to learn quickly is important.
Data modeling tool customization. In many cases model and DDL. generation is controlled by scripts in the data modeling tool. These scripts control the generation of many aspects of the data model including how data auditing is carried out and how XSD models are generated from the physical model. Maintaining these scripts and version upgrades to the data modeling tool is a key activity.
Documentation. A central data portal details all the JPAM data standards. Keeping this information up to date and relevant is an important task.
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