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AVP, Marketing Data Engineering

AXOS Financial INC - San Diego, CA

Posted May 12, 2026

Benefits

Parental leave
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Non-birth-parent leave
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Family-building benefits
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Learning budget
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Verification
Not verified last checked Jun 13, 2026
Salary
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401(k) match
Listed Source: EMPLR_CONTRIB_INCOME_AMT. source Last checked Jun 13, 2026.

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Schedule

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Weekend work
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Application

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About this role

AVP, Marketing Data Engineering San Diego, CA Axos Bank Target Range: $115,000.00/Yr. - $150,000.00/Yr. Actual starting pay will vary based on factors including, but not limited to, geographic location, experience, skills, specialty, and education. Eligible for an Annual Discretionary Cash Bonus Target: 10% Eligible for an Annual Discretionary Restricted Stock Units Bonus Target: 10% These discretionary target bonuses may be awarded semi-annually based upon your achievement of performance goals and targets. About This Job The AVP, Marketing Data Engineering leads the design and operation of scalable, reliable data pipelines that power marketing activation and measurement. This role owns how marketing data is ingested, processed, and delivered, partnering closely with analytics, governance, and platform teams. This is a hands-on leadership role with accountability for both architecture and execution. Responsibilities: Design and operate secure batch and near‑real‑time data ingestion pipelines Ensure raw data is immutable, traceable, and standardized for downstream use Build transformation layers and canonical data models that protect downstream systems Own pipeline reliability, monitoring, and data quality controls Lead and scale hybrid onshore/offshore engineering teams Establish runbooks, quality standards, and escalation paths Partner with analytics, data governance, MarTech, and activation teams Drive automation and AI adoption to improve quality, efficiency, and scalability Qualifications: Bachelor's degree preferred 7+ years in data engineering or data platform roles Experience leading engineering teams, including distributed or offshore models Strong experience with modern data platforms (e.g., Databricks or similar) Hands-on expertise in data ingestion, transformation, and quality monitoring Experience in regulated environments (financial services preferred)

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