Consumer Bank - Data Domain Architect - Vice President
JPMorgan Chase
- Columbus, OH
- Permanent
- Full-time
- Lead intelligence solution requirements gathering sessions with varying levels of leadership, complete detailed project planning utilizing JIRA to record planned project execution steps
- Source and maintain the Financial and Operational data required to transform and maintain the Field Performance Reporting scorecards on a weekly basis
- Conduct detailed reconciliation of results between systems and intelligence solution
- Generate detailed metadata on existing and new data models
- Partner in the development of data wrangling workflows, data visualization solutions utilizing ThoughtSpot and Tableau and other end-user reporting interfaces and that provides intuitive insights to our key stakeholders
- Work closely with end-users/IT during the UAT phase of the project and validate that production results comply with business requirements and expected results
- Seek to become a subject matter expert and understand the stakeholder use cases empowering you to anticipate stakeholders requirements, questions, and objections
- Data Modeling and Design: Proficiency in designing data models that effectively represent the business requirements and ensure data integrity.
- Data Governance: Understanding of data governance principles, policies, and practices to ensure data quality, security, and compliance.
- Database Management: Expertise in managing various types of databases (SQL, NoSQL) and understanding of their strengths and limitations.
- Data Integration and ETL: Knowledge of techniques for extracting, transforming, and loading (ETL) data
- Data Warehousing: Understanding of data warehousing concepts and knowledge of design principles
- Data Quality Management: Understanding of strategies and tools for data profiling, cleansing, and validation to maintain high data quality.
- Big Data and Analytics: Awareness of technologies and frameworks for handling large volumes of data (e.g., Hadoop, Spark) and knowledge of analytical tools.
- Data Visualization: Proficiency in using visualization tools (e.g., Tableau, Power BI) to create meaningful insights from data.
- Technical Proficiency: Strong understanding of programming languages relevant to data manipulation and analysis (e.g., SQL, Python, R).
- Domain Knowledge: Deep understanding of the specific industry or business domain in which the organization operates.
- Communication Skills: Ability to effectively communicate complex technical concepts to non-technical stakeholders.
- Leadership Skills: Demonstrated leadership abilities with track record of driving results through a matrix structured organization. Highly motivated, self-directed and ability to project management dynamic timelines
- Problem Solving and Critical Thinking: Capacity to identify and solve data-related challenges, as well as anticipate future needs and trends.
- Team Collaboration: Skill in working collaboratively with cross-functional teams including data scientists, engineers, analysts, and business stakeholders.
- Continuous Learning: Commitment to staying updated with emerging technologies and best practices in the data management field.
- Minimum of 7 years of experience in data architecture, data warehousing, and data integration, with a focus on the retail banking or financial services industry
- Bachelor's degree in MIS or Computer Science/IT, Mathematics, Engineering, Statistics or other quantitative or financial subject areas
- Experience with relational databases optimizing SQL to pull and summarize large datasets, report creation and ad-hoc analyses
- Experience with business intelligence analytic and data wrangling tools such as Alteryx, SAS, or Python. ThoughtSpot, Tableau, Alteryx, or Essbase
- Experience with Hive, Spark SQL, Impala or other big-data query tools
- Strong knowledge and experience with data management, data lineage, data dictionaries, and making data discoverable
- Experience in reporting development and testing, and ability to interpret unstructured data and draw objective inferences given known limitations of the data
- Hyperion Essbase cube design and administration experience
- Strong knowledge of AWS and Amazon S3 Data Lake. Databricks, Snowflake, or other Cloud Data Warehouse experience
- Experience on market leading data catalog systems
- Strong command of the English language (written and verbal)
- Team player