Data Solutions Engineering Director

4 weeks ago

New York, New York, United States Conde Nast Full time

Conde Nast is a global media company renowned for producing high-quality content for influential audiences, attracting over 100 million consumers across its industry-leading print, digital, and video brands.

The company's portfolio includes respected media properties like Vogue, Vanity Fair, Glamour, and Wired, among others.

We are seeking a VP of Data Solutions Engineering to lead our global data engineering and data warehousing practice, driving the development of a customer 360 initiative across revenue lines of business.

As a seasoned data engineering professional, you will spearhead the strategic planning, organizational management, and execution of our data engineering roadmap, collaborating with cross-functional teams to deliver a unified customer view and enable data-driven decision-making.

Responsibilities:

  • Lead and manage a diverse team of data engineers, fostering a culture of collaboration, innovation, and continuous growth.
  • Build and optimize the organizational structure of the global data engineering practice, aligning it with core business functions and global growth objectives.
  • Design and build future data assets focused on unifying consumer data across revenue streams to understand how best to segment consumers and tailor the right experience.
  • Partner with business department heads to drive data-driven solutions and maximize revenue opportunities.
  • Forge strong partnerships with cross-functional technology and core engineering teams to create integrated roadmaps and align on the strategic vision.
  • Develop and execute a global data engineering roadmap, closely tied to product objectives and global engineering principles.
  • Lead the revamp of our internal first-party global data collection strategy, ensuring compliance with data privacy regulations like GDPR.
  • Collaborate with Data Science to build a next-generation ML infrastructure for feature engineering and model serving.
  • Work with business intelligence and product analytics teams to build data marts to speed up time to decision-making.
  • Implement company-wide data quality frameworks, ensuring high-quality data across all data engineering pipelines and data warehousing models.
  • Foster strong partnerships with technology vendors, negotiating and managing technical contracts to support the organization's needs and ensure cost-effective solutions.
  • Stay updated with industry trends and advancements in data engineering, evaluating and recommending suitable technologies and tools.

Qualifications:

  • Bachelor's or Master's degree in Computer Science, Engineering, or a related field. An advanced degree is preferred.
  • Extensive experience in data engineering, big data management, and digital technology, with a focus on media and analytics.
  • Proven track record of building and scaling teams, platforms, and products at an enterprise level.
  • Experience in architecting data products using streaming, serverless, and microservices-based architectures and platforms.
  • Proficiency in software development using Python, Scala, or a similar programming language, with experience in data structures, algorithms, and software design.
  • Strong expertise in cloud platforms, preferably AWS, and data processing frameworks like Spark on the Databricks data platform.
  • In-depth knowledge of data analysis, data governance, data privacy regulations, and data quality frameworks.
  • Experience with ML infrastructure, feature engineering, and model serving.
  • Excellent leadership and management skills, with the ability to mentor and develop high-performing teams. Expertise in budget control and vendor management preferred.
  • Strong strategic planning and execution abilities, with a focus on aligning data engineering with business objectives.
  • Effective communication and stakeholder management skills.

The expected base salary range for this position is from $250,000 - $300,000. Salary offers are based on a wide range of factors, including relevant skills, training, experience, and education.

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