Staff Engineer: AI Telemetry & Developer Insights

Capital One; New York (NY); On-site; USD 294,000 - 335,000; Full time

Job summary

Capital One is hiring a Staff Data Engineer to shape the future of developer telemetry and AI-enabled data platforms in the cloud. You will architect scalable pipelines, lead cross-functional teams, and mentor senior engineers while advancing real-time analytics and privacy-by-design practices.

The role emphasizes strategic tech leadership, prototyping, code patterns, and collaboration across data engineering, ML, and product teams at multiple locations.

Qualifications

• Bachelor's degree in Computer Science or a related field.
• At least 7 years of experience in data engineering.
• At least 3 years in data architecture.
• At least 2 years building applications in AWS.
• At least 5 years of experience in programming (Python/Java/Scala).
• At least 5 years in data pipelines and data modeling.

Responsibilities

• Architect the long-term enterprise roadmap for distributed AI telemetry and real-time data pipelines.
• Lead evolution of internal telemetry platform for billions of events across regions.
• Prototype and deploy ML/AI capabilities to score code complexity and ROI of AI tools.
• Own end-to-end data strategy from ingestion to real-time lake with a Medallion structure.
• Partner with analytics to tie AI telemetry to business outcomes and risk controls.
• Design privacy-by-design patterns with automated PII masking and secret management.

Skills

Python

Java

Scala

Data engineering

Data modeling

AWS

SQL

Streaming data

Education

Bachelor's Degree in CS

Master’s Degree in CS

Tools

EMR

Spark

Glue

Databricks

Snowflake

Job description

Capital One is hiring a Staff Data Engineer to shape the future of developer telemetry and AI-enabled data platforms in the cloud. You will architect scalable pipelines, lead cross-functional teams, and mentor senior engineers while advancing real-time analytics and privacy-by-design practices.

The role emphasizes strategic tech leadership, prototyping, code patterns, and collaboration across data engineering, ML, and product teams at multiple locations.