Data Engineer II
1 World Trade Center, New York, NY
Condé Nast
Location:
New York, NYCondé 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.
The Data Solutions Engineering team have a wide range of responsibilities and play a
critical role in shaping how Condé Nast enables its business using data. The team is
responsible for building data pipelines, data products and tools that enable our Data
Scientists, Analysts in various business units, Business Intelligence Engineers and Executives
to solve challenging use cases in our industry.
We are seeking a Data Engineer who will build and maintain data pipelines across business
areas such as subscriptions, video, clickstream, commerce, social and advertising within
Condé Nast. If you are looking for a challenging environment and to work with a world class
team of data engineers in a well balanced environment and seasoned company, come join
us:
RESPONSIBILITIES
Responsibilities include, but are not limited to:
● Design, build and test batch and streaming data pipelines
● Build efficient code to transform raw data into datasets for analysis, reporting and
machine learning models
● Work with data scientists to deploy machine learning model outputs into CEPs for
recommendations and personalizations
● Collaborate with other data engineers, data scientists and product managers to
implement a shared technical vision
● Participate in the entire software development lifecycle, from concept to release
● Contribute to shared data engineering tooling & standards to improve productivity
● Support, monitor and optimize current and future data infrastructure and platform
● Provide technical guidance and mentorship to junior engineers
● Evaluate technologies and conduct proof-of-concepts
MINIMUM QUALIFICATIONS
● Applicants should have a degree (B.S. or higher) in Computer Science or a related
discipline or relevant professional experience
● 3+ years of software development experience designing scalable & automated
software systems
● Experience in processing structured and unstructured data into a form suitable for
analysis and reporting
● Experience with data modelling, batch data pipeline design and implementation
● Experience building batch or real-time data pipelines
● Strong software development skills with high proficiency in Python/PySpark or Scala
● High Proficiency in SQL
● Experience with data processing frameworks such as Apache Spark (we use
Databricks)
● Experience with data transformation tools such as dbt Cloud
● Experience in cloud-based infrastructures such as AWS or GCP
● Exposure to orchestration platforms such as Airflow (we use Astronomer)
● Proven attention to detail, critical thinking, and the ability to work independently
within a cross-functional team
● Comfortable with CI/CD (we use GitHub Actions) Pipelines
● Experience with Git version control, and other software adjacent tools
● NYC Pay Range: $140,000.00-155,000.00
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.
Tags: Airflow AWS Business Intelligence CI/CD Computer Science Databricks Data pipelines dbt Engineering GCP Git GitHub Machine Learning ML models Pipelines PySpark Python Scala Spark SQL Streaming Unstructured data
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