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Full time
Technology
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Principal Data Architect

Cox AutomotiveAustin, Texas🇺🇸United StatesPosted 10 Sept 2026

Why This Role Stands Out

This Principal Data Architect role offers a unique opportunity to shape the data vision for leading brands like Autotrader and Kelley Blue Book, driving innovation in cloud-native data platforms and influencing technical direction. You'll thrive here if you're a seasoned architect passionate about building scalable data solutions and mentoring teams in a hybrid environment that fosters collaboration and growth. Apply now to make a significant impact on Cox Automotive's data strategy and advance your leadership career.

Quick Overview

Seniority
Leader
Employment type
Full Time
Work mode
Hybrid
Location
Austin, Texas, United States
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Job Description

Cox Automotive is seeking a Principal Data Architect to lead the data architecture vision for our telecommunications and media-focused platforms. In this role, you will define enterprise data strategies, design cloud-native data platforms, and create standards for modeling, governance, and integration across brands like Autotrader, Kelley Blue Book, and Manheim. You'll collaborate with engineering, product, and analytics teams to deliver secure, scalable, and high-performing data solutions that power advanced analytics and digital products, while mentoring technical teams and shaping our long-term data roadmap.

Responsibilities

  • Define and own enterprise data architecture for telecom and media solutions
  • Design scalable cloud data platforms and reference architectures
  • Establish data modeling, governance, and integration standards
  • Partner with product, engineering, and analytics teams on data strategy
  • Evaluate and select data technologies and tooling
  • Ensure data security, privacy, and compliance
  • Optimize performance, reliability, and cost of data solutions
  • Mentor engineers and influence technical direction across brands

Required Skills

  • Enterprise data architecture
  • Cloud data platforms (AWS/Azure/GCP)
  • Data modeling (relational and dimensional)
  • Data warehousing and lakehouse design
  • Data integration and ETL/ELTData governance and metadata management
  • SQL and distributed query engines
  • Big data frameworks (e.g., Spark)
  • API and event-driven data integration
  • Data security, privacy, and compliance

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