Find Jobs
Find Jobs Near You – Available Work in Your Location
Skip to job details
CO
Capital One
Sr Manager IC, Merchant & Advertiser Data Marts - Analytics Team (Remote-Eligible)
Career Insights for Data Manager
See where this job fits in the broader career landscape. Knowing your career path helps you see what's possible from here.
Scorecard
Based on Illinois data
Review key factors to help you decide if this role fits your goals. How is this calculated?
What they do
A Data Manager manages databases and coordinates data collection and analysis for a company or organization. Develops procedures for documentation and data storage. Performs or manages data analysis for studies, projects and reports.
$143,234 / year median in Illinois
+9% projected growth
Job Description
Sr Manager IC, Merchant & Advertiser Data Marts - Analytics Team (Remote-Eligible)The roleOwn the platform substrate behind Capital One Shopping's merchant and advertiser data - the marts that power Retail Media, advertiser reporting, revenue attribution, and every marketing measurement in the business. You set the standards (reference architecture, catalog, access policy, GDPR/audit guarantees) that downstream product teams build on. This is an own-and-build role, not a maintenance seat: you own and operate these marts in production, and in your first six months you ship three net-new systems. You'll report to the Engineering Director for the Shopping data platform as one of two senior IC pillars of the analytics org.
What you'll buildA data mart catalog + query-pattern analysis - a single source of truth covering every production mart (owner, SLO, lineage, PII classification, audit trail), plus a Trino-log analysis that shows which raw tables need a mart next. Extends the existing Dataset Registration Scanner, doesn't fork it.
Iceberg standardization on a revenue-attached mart - finish the Hive → Iceberg pattern on one high-value merchant/advertiser mart, with PII-masking parity, GDPR deletion-propagation, and schema-change audit baked into a reusable playbook other teams can follow. Bounded standardization, not an open-ended migration.
A marts-first access policy, enforced - draft and land a policy that steers consumers off raw event tables when a mart exists, enforced on three high-traffic raw schemas via Trino permissions + CloudSentry, with a full auditable exception log. The stackLakehouse on Iceberg (with legacy Hive tables mid-standardization), dbt at multi-repo scale, Trino/Presto and Spark SQL, Airflow orchestration, Kafka/MSK streaming upstream, on AWS. Data catalog tooling (Amundsen, DataHub, Collibra, OpenMetadata, or home-grown equivalents).
The day-to-day\
What you'll buildA data mart catalog + query-pattern analysis - a single source of truth covering every production mart (owner, SLO, lineage, PII classification, audit trail), plus a Trino-log analysis that shows which raw tables need a mart next. Extends the existing Dataset Registration Scanner, doesn't fork it.
Iceberg standardization on a revenue-attached mart - finish the Hive → Iceberg pattern on one high-value merchant/advertiser mart, with PII-masking parity, GDPR deletion-propagation, and schema-change audit baked into a reusable playbook other teams can follow. Bounded standardization, not an open-ended migration.
A marts-first access policy, enforced - draft and land a policy that steers consumers off raw event tables when a mart exists, enforced on three high-traffic raw schemas via Trino permissions + CloudSentry, with a full auditable exception log. The stackLakehouse on Iceberg (with legacy Hive tables mid-standardization), dbt at multi-repo scale, Trino/Presto and Spark SQL, Airflow orchestration, Kafka/MSK streaming upstream, on AWS. Data catalog tooling (Amundsen, DataHub, Collibra, OpenMetadata, or home-grown equivalents).
GDPR/CCPA
governance mechanics throughout.The day-to-day\