Wholesale Data Domain & Analytics
The CCB Data & Analytics Team unifies data and analytics talent across Chase to responsibly leverage data to build competitive advantages for our businesses with value and protection for customers. The team encompasses a variety of Data & Analytics disciplines, from data governance and data strategy/partnerships to reporting, data science and machine learning, and are actively engaged in ensuring impact at the front-line and the customer through Sales and Marketing transformation. We have a strong partnership with our dedicated Technology partners, who provide us with our cutting edge data and analytics infrastructure. Joining CIB Data & Analytics means you sit in the engine that powers Chase with insights, providing an opportunity to materially impact both our customer and business outcomes. The team also offers significant learning and mobility opportunities for career development and future growth.
Data Domain Owner within the CCB & CIB Data Infrastructure focus on Data Management and supporting our Decision Science and Analytics Partners. They drive agile development, migration of processes and data from Business Analysts and Data Science Teams to Technology Supported Platforms and Operations. They need to be the bridge between Data Scientists and Technology Production and Operations, engaged heavily with designing and implementing solutions that are scalable and satisfy analytics performance requirements across environments.
The Wholesale Payment and Consumer Banking Data Domains are responsible for driving critical cross-functional projects in support of designing, building, and implementing business customer-centric data assets for use across the firm. The candidate must exhibit a thorough understanding of data structures, data manipulation, metadata, data security, and data quality management. In addition, the candidate must have a solid understanding of CCB & CIB businesses, functions, systems, data environments, and processes that are necessary for the production and utilization of customer data.
As a Data Domain Owner, you will work as a consultative team member in driving analytical solutions and adherence to Firmwide data governance. The candidate must have the ability to partner with stakeholders inside and outside of the department to understand their needs and actively engage in the design and development of robust data solutions that meet and/or exceed customer expectations. The ideal candidate will possess JPM Chase institutional data and system knowledge, technical skills, an understanding of data science, and a commitment to producing high quality results. Experience with leading teams delivering solutions through agile methodology and taking on a Product Owner role will be a strong plus.
Other responsibilities will include the following:
Conduct business process analysis and identify data needed to support the processes and determine whether the firm’s data is fit for use within a given process
Facilitate use case & requirements definition, design, testing, and implementation of new data assets and analytic capabilities that address specific business needs
Guide analytical teams in following and adhering to Firmwide data governance policies
Support technology data modelers towards the creation of conceptual & logical models to describe a particular domain of data and use these models to inform the physical design of data-related projects in support of information governance
Enable the management of data as a corporate asset: define data (metadata), identify systems of record and authoritative sources, create data quality rules, define security requirements, create data flow diagrams, and administer firm-wide principles, standards, and controls
Participate in the design and architect solutions with IT teams, scope out new engagements and implementations both short and long term, and guide project teams on business priority and impact during product implementations
Develop and maintain strategic roadmaps for the analytical data domains that describe a sequence of projects to improve management and utility of the data for the business
Identify areas for efficiency across data domains, such as the elimination of duplicate data or platforms
Conduct research and development with emerging technologies, determine their applicability to business use cases, document & communicate their recommended use in the firm
Minimum five years of Decision Science related Applications Architect experience – technology implementation experience and certifications a plus
5+ years working with big data environments such as MapReduce, Hive, SparkSQL, Sqoop, HDFS, and Spark; Hadoop experience is preferred
Write data transformation logic in languages such as Python, SAS, Spark, or Scala
Coding skills to profile, wrangle, and prepare data from diverse sources to support analytical efforts
BI Experience a must (Tableau, Cognos, Qlik, BO, SSAS)
Bachelor’s degree in Computer Science, MIS, Software Engineering, or a related discipline
10+ year of experience in managing and implementing data management programs in the financial services space. The experience can be related to key programs such as OCC, BCBS, SOX and CCAR
7+ years of experience as a lead or key participant on data infrastructure, data governance and data management related efforts
3+ years of Professional Services (customer facing) experience architecting large scale storage, data center and /or globally distributed solutions
Proven experience with complex data infrastructure initiatives, best practices and key components
2+ years of experience in using Big Data or Cloud based Consumption OLAP tools (AtScale, Kyvos or other OLAP on Hadoop, Snowflake, AWS tools) required
Ability to understand big data use-cases and recommend standard design patterns commonly used in Hadoop-based deployments
Ability to create and evaluate conceptual & logical models to describe a particular domain of data and use these models to inform the physical design of data-related projects
In-depth knowledge of Wholesale Payment industry
Technical understanding of common RDBMS systems; (e.g. Teradata, Oracle)