Senior Manager, Data Analysis, Network Platforms and Product Operations

Capital One Financial

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Quick summary

Work type: On-site
Location: New York, NYRichmond, VAMcLean, VAChicago, IL
Salary: $182,500–$208,300 / yr
Employment: Full-time
Posted: 119 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $191k

This role $195k

$146k $239k

below market most similar roles pay here above market

This role pays less than 55% of similar roles. Most pay $165,000–$216,587 — the blue band above. At the midpoint, this role pays about $195k versus about $191k for comparable roles.

Based on 240 similar postings.

Employer

About Capital One Financial

Capital One Financial is a bank holding company specializing in credit cards, auto loans, banking, and savings products, known for its data-driven approach to consumer and commercial finance. Industry: Financial Services & Banking

Capital One Financial currently has 2107 open roles on FindRole.

Listed pay typically runs $197,300–$225,100 across 1107 roles with salary data.

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View all roles at Capital One Financial

At a glance

TL;DR · Senior Manager, Data Analysis, Network Platforms and Product Operations

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The Senior Manager, Data Analysis - Network Platforms and Product Operations joins the Platform Operations Tower to serve as a technical architect and strategic leader. This role involves transforming high-volume transactional data streams into core analytical assets for network platforms, value-added services, and servicing portals. Key responsibilities include owning data strategy, establishing technical roadmaps for monitoring and anomaly detection, optimizing backend operational data schemas, and coaching technical staff. The position requires managing complex technical issues in network data while assessing risks and operational optimization opportunities. Candidates must possess skills in open source data technologies, statistical or econometric modeling, predictive analytics, and time-series forecasting. The work focuses on processing real-time transactional logs, such as auth and settlement data, within a financial services environment to ensure data quality and scalability.

Skills

Data Analysis Data Architecture Statistical Modeling Predictive Analytics Time-Series Forecasting AI/ML Data Governance Cloud Infrastructure Anomaly Detection Open Source Data Technologies Dashboards

What you'll do

Own the data strategy, architecture, and modeling frameworks for core network platforms and servicing portals.
Establish the technical roadmap for monitoring and anomaly detection across the Network Platforms and Products tower.
Optimize complex backend operational data schemas and refine core monitoring frameworks.
Assess the quality, scalability, and performance of real-time transactional data logs across product domains.
Identify technical gaps in network data and proactively assess data-driven risks and operational optimization opportunities.
Align local data assets with enterprise data strategy, cloud infrastructure, and global governance mandates.
Coach and mentor technical staff while setting high engineering standards for data utilization.
Communicate technical results and data trends through operational reports, dashboards, and executive presentations.

What we're looking for

Bachelor’s Degree in a quantitative field plus 7 years of data analytics experience, or a Master’s Degree plus 5 years of experience.
At least 5 years of experience performing professional data analysis work.
At least 5 years of experience leading and developing with open source data technologies.
At least 2 years of experience managing people.
Master’s Degree or PhD in Data Science, Computer Science, Statistics, Mathematics, Operations Research, or a related field (preferred).
At least 7 years of hands-on experience working with high-volume, semi-structured transactional and application log data (preferred).
At least 5 years of experience manipulating, optimizing, and performing analysis with massive, enterprise-scale datasets (preferred).
At least 5 years of experience in financial services industry (preferred).

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