Lead Data Engineer - 1015
Arthur C. Nielsen founded the company in 1923 with a goal of helping companies measure their sales and better understand the consumers that drove them. From retail sales and consumer purchase measurement to Radio and Television measurement, Nielsen is the original big data company. Digital consumer measurement is the latest chapter in our rich measurement history
We are the provider of the Web’s primary measurement platform for brand advertising (a $83B industry in 2017 and growing quickly). Some of our customers: Procter & Gamble, Facebook, Hulu, GroupM, and Roku. Strong strategic technology partnerships with Facebook, Google, Adobe, and others set our measurement products apart from any others in the industry. We are an open-minded and innovative engineering team and environment, using many of the latest open-source software, frameworks, tools, and cloud computing services
As Lead Data Engineer, you will leverage your past experience building and managing production platforms to lead and oversee platform and software development in Nielsen Data Science.
Work within the Data Science organization to exert technical influence over multiple teams, increasing their productivity and effectiveness by sharing your deep knowledge and experience in data engineering, software development, cloud infrastructure, and automation.
Drive technical solutions to complex business problems. From lightweight automation to designing and building robust/scalable solutions. Strong focus on optimization.
Contributing to team discussions around data modeling / architecture, application architecture, and software development lifecycle.
Possess expert knowledge in performance, scalability, enterprise system architecture, and engineering best practices.
What kind of engineer are we looking for?
Degree in Statistics, Economics, Applied Mathematics, Computer Science, Engineering, or other Quantitative field of study
5+ years of data engineering experience / software development experience
Expert knowledge of Python, Spark, Scala
Expert knowledge of the Hadoop ecosystem
Experienced with AWS / Azure and Infrastructure as Code
Experience with ETL pipelining and data warehousing
Strong knowledge of Git workflows, particularly in a collaborative environment
Familiarity with Airflow, Ansible, Docker, or Kubernetes is a plus
Experience with relational database management systems (RDBMS) and knowledge of SQL
Exceptional problem solving skills
Excellent oral and written communication skills. Well organized and capable of handling multiple mission critical projects simultaneously while meeting deadlines
Self-motivated and an ability to handle multiple competing priorities in a fast-paced environment
Manage projects consisting of cross functional teams including data science, engineering, and product leadership