VP, Data Solutions Engineering

1 World Trade Center, New York, NY

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Condé Nast is a global media company producing the highest quality content with a footprint of more than 1 billion consumers in 32 territories through print, digital, video and social platforms. The company’s portfolio includes many of the world’s most respected and influential media properties including Vogue, Vanity Fair, Glamour, Self, GQ, The New Yorker, Condé Nast Traveler/Traveller, Allure, AD, Bon Appétit and Wired, among others.

Job Description

Location:

New York, NY

Condé Nast is a premier media company renowned for producing the highest quality content for the world's most influential audiences, attracting more than 100 million consumers across its industry-leading print, digital and video brands.
Condé Nast is home to many of the world's most-celebrated magazine and website brands. The company's reputation for excellence is the result of our commitment to publishing the best consumer, trade, and lifestyle content. Our brands include Vogue, Epicurious, Vanity Fair, The New Yorker, Wired, and many more. Passion is the core of our philosophy at Condé Nast. Our mission is not only to inform readers but to ignite and nourish their passions.

We are seeking a VP of Data Solutions Engineering to lead our global data engineering & data warehousing practice and drive the development of a customer 360 initiative across our revenue lines of business. Your extensive experience in data engineering, big data management, media, and digital technology will be instrumental in building and scaling our data platforms and products while ensuring compliance with data privacy regulations. 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
Responsibilities include, but are not limited to:
● 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 our consumers data across revenue streams to understand how best to segment consumers to tailor the right
experience to deliver
● 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 the 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
● Negotiate and manage 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 but not limited to relevant skills, training, experience, and education.

What happens next?

If you are interested in this opportunity, please apply below, and we will review your application as soon as possible. You can update your resume or upload a cover letter at any time by accessing your candidate profile.

Condé Nast is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, age, familial status and other legally protected characteristics.

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Job stats:  18  2  0
Category: Engineering Jobs

Tags: Architecture AWS Big Data Business Intelligence Computer Science Data analysis Databricks Data governance Data management Data quality Data Warehousing Engineering Feature engineering Machine Learning Microservices ML infrastructure Pipelines Privacy Python Scala Spark Streaming

Perks/benefits: Career development Startup environment

Region: North America
Country: United States

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