Director, Marketing Data Engineering

The NAOU Marketing Data team is responsible for building the data foundation, engineering capabilities, and analytics solutions that enable better marketing decisions across North America. The team connects data, technology, and insights to create scalable capabilities that support consumer understanding, marketing effectiveness, measurement, personalization, and AI-driven decision-making. Working across Marketing, Integrated Marketing Experience (IMX), Human Sciences, Advanced Analytics, MarTech, Digital Technology, and enterprise data teams, the organization helps transform data into a strategic asset that drives growth, innovation, and business impact. Role Overview The Director, Data Engineering will lead the hands-on design, development, and operation of scalable marketing data pipelines and curated data products across Microsoft Azure. The role transforms internal and external data into trusted, governed, reusable assets for analytics, measurement, activation, personalization, and AI-enabled decision-making. This player-coach will set engineering standards while actively designing solutions, writing and reviewing code, and resolving complex issues. The role partners with Marketing Data Architecture, Marketing Analytics, MarTech, Digital Technology, and enterprise data teams to deliver secure, reliable, observable, and cost-effective capabilities. What You Will Do for Us Design & Build Azure Data Pipelines: Personally design, code, test, deploy, and operate scalable batch and streaming pipelines across Azure. Integrate consumer, media, commerce, CRM, loyalty, Adobe, and enterprise data sources using reusable engineering patterns. Curate Trusted, AI-Ready Data Products: Build standardized, documented datasets and data products that are accurate, discoverable, reusable, and ready for analytics, machine learning, and generative AI. Apply strong data modeling, metadata, lineage, and data contract practices. Lead Hands-On Engineering & Technical Design: Translate business and architecture requirements into production-grade solutions. Create technical designs, write and review Python, SQL, and PySpark code, troubleshoot complex issues, and balance speed, scale, quality, and maintainability. Establish GitHub Engineering & DevOps Practices: Use GitHub for source control, pull requests, code reviews, documentation, and collaboration. Implement automated testing, CI/CD, infrastructure as code, release management, and secure development practices. Use AI-Assisted Development Responsibly: Use Codex, Cursor, and GitHub Copilot to accelerate design, coding, testing, refactoring, and documentation. Establish validation and security guardrails so AI-generated code meets enterprise engineering, privacy, and quality standards. Ensure Reliability, Quality & Operational Excellence: Build monitoring, observability, alerts, data quality controls, and service expectations into every pipeline. Lead incident response and root-cause analysis while optimizing performance, scalability, security, and cloud cost. Partner, Deliver & Develop Engineering Talent: Partner across Marketing, Analytics, Architecture, MarTech, and Digital Technology to deliver high-value capabilities. Mentor engineers through hands-on pairing and code reviews, raising standards and fostering accountability, curiosity, and continuous learning. Qualifications Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field; advanced degree preferred. 10+ years of hands-on data engineering or platform experience, including technical leadership of production-scale solutions and mentoring engineers. Expert SQL and strong Python/PySpark skills across data modeling, ETL/ELT, distributed processing, orchestration, quality, observability, and performance tuning. Hands-on experience with Azure data services such as Data Factory, Fabric, Databricks, Data Lake Storage, Synapse Analytics, Functions, or Event Hubs. Strong GitHub and DevOps experience with code review, automated testing, CI/CD, infrastructure as code using Terraform or Bicep, and automated deployment. Practical experience with Codex, Cursor, or GitHub Copilot, including disciplined validation of generated code, tests, security, and documentation. Experience engineering consumer, media, customer, commerce, or marketing data from 1st party, 2nd party, and 3rd party sources in a matrixed organization. All persons hired will be required to verify identity and eligibility to work in the United States and to complete the required employment eligibility verification form (Form I-9) upon hire. Apache Spark, Data Analysis, Data Engineering, Data Governance, Data Strategies, Design, Microsoft Cloud, Privacy Compliance Pay Range: United States: 169,000 - 200,000 USD Base pay offered may vary depending on geography, job-related knowledge, skills, and experience. A full range of medical, financial, and/or other benefits, dependent on the position, is offered. Annual Incentive Reference Value