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À propos
Job Overview:
We are seeking a Data Analyst to support the US Card Partnerships team, focusing on credit card analytics, brand partnership performance, and customer insights.
Required Skill:
5+ years of experience
in data analytics, preferably in the
financial services or credit card domain . Prior experience supporting
Capital Ones Credit Card Data Analytics
or similar environments is strongly preferred. Advanced SQL
expertise including window functions, subqueries, joins, and optimization. Hands-on experience with
Snowflake, PostgreSQL, and Databricks . Familiarity with
AWS S3
for data ingestion and loading. Strong proficiency in
Python
or
PySpark
for data wrangling and analytical scripting. Proficiency in
Tableau
for dashboard creation and visual storytelling; understanding of
context filters
and
data connection strategies
(Extract vs. Live). Working knowledge of
predictive analytics models , classification techniques, or LLM-based analysis (preferred but not required). Strong problem-solving and analytical thinking skills with the ability to translate data into actionable insights. Preferred:
Familiarity with
credit card portfolio analytics ,
brand partnership analysis , and
customer journey mapping . Understanding of
machine learning models
or
predictive analytics
frameworks used in customer segmentation and transaction analysis. Job Description:
Analyze large-scale transactional and customer datasets to uncover insights on spending patterns, portfolio performance, and campaign effectiveness. Work on
brand partnership analytics
identifying brand-specific customer behavior trends, transaction anomalies, and performance indicators. Perform
data extraction, transformation, and loading (ETL)
using
SQL, Snowflake, Databricks, and AWS S3
environments. Develop and optimize complex
SQL queries
(including Window Functions, Partitions, Rank/Dense Rank/Row Number). Use
Python or PySpark
for data manipulation, automation, and predictive analytics. Create and maintain
Tableau dashboards
and visual reports for business stakeholders. Apply
context filters
and efficient data connections (Extract vs. Live) to optimize dashboard performance. Collaborate with cross-functional teams to design data-driven solutions for customer segmentation, personalization, and portfolio monitoring. Support ad-hoc analytical requests and reporting related to customer journey mapping and credit card usage trends.
Compétences linguistiques
- English
Avis aux utilisateurs
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