Senior Data Analyst - Global Payment Network
Role details
Job location
Tech stack
Job description
At Capital One, data is at the center of everything we do. When we launched as a startup we disrupted the credit card industry by individually personalizing every credit card offer using statistical modeling and the relational database, cutting edge technology in 1988! Fast-forward a few years, and this little innovation and our passion for data has skyrocketed us to a Fortune 200 company and a leader in the world of data-driven decision-making.
GNA stands for Global Network Analytics within Discover's Global Payment Network (GPN) business. The GNA department provides analytical solutions to payment businesses covering the whole lifecycle of participant management. This department has a variety of teams that handle a plethora of topics including but not limited to: marketing, pricing, fraud, optimization, automation, restitution, and even GenAI and text modeling.
On any given day you will be challenged on three types of work - Innovation, Business Intelligence and Data Management:
Innovation
- Use Open Source/Digital technologies to mine complex, voluminous, and different varieties of data sources and platforms
- Build well-managed data solutions, tools, and capabilities to enable self-service frameworks for data consumers
- Demonstrate ability to explore and quickly grasp new technologies to progress varied initiatives
Business Intelligence
- Partner with the business to provide consultancy and translate the business needs to design and develop tools, techniques, metrics, and dashboards for insights and data visualization
- Drive analysis that provides meaningful insights on business strategies
Data Management
- Drive an understanding and adherence to the principles of data quality management including metadata, lineage, and business definitions
- Work collaboratively with appropriate Tech teams to manage security mechanisms and data access governance
- Build and execute tools to monitor and report on data quality
Requirements
- Degree specialized in a Science, Technology, Engineering, Mathematics discipline
- Scripting experience in (Python, R, Spark, and SQL)
- Strong desire and experience with data in various forms (data warehouses/SQL, unstructured data)
- Experience utilizing and developing within AWS services, * Currently has, or is in the process of obtaining a Bachelor's Degree in quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science or a related quantitative field) plus at least 2 years of experience performing data analytics, or currently has, or is in the process of obtaining a Master's Degree with an expectation that required degree will be obtained on or before the scheduled start date.
- At least 1 year of experience working with at least one scripting language
- At least 1 year of experience utilizing business intelligence visualization tool
- At least 1 year of experience querying, analyzing and working with data languages and platforms, * Master's Degree in a Science, Technology, Engineering, Mathematics discipline
- At least 2 years of experience coding in Python, R, Spark, or SQL
- At least 2 years of experience working within process management and improvement methodologies - Agile, Lean, Six Sigma, etc.
- At least 1 year of experience utilizing and developing within AWS services
- At least 1 year of experience working with and demonstrating knowledge of data governance, data quality management concepts and data quality tools (i.e. Informatica DQ)
Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.
This role is Hybrid, with associates expected to consistently spend three days per week in the office.
The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.