Big Data Engineer at Samba TV in San Francisco
Samba TV is uniquely positioned at the forefront of the TV revolution. The way people discover, watch, and engage with television has fundamentally changed, and we’re connecting the dots to help better understand audience trends and viewership habits for marketers.
Core Viewership is responsible for the data processing pipelines that produce key datasets consumed by our data scientists, research analysts, and external customers, as well as power our analytics platform. This team deals with data at scale — on a continuous basis, we ingest, process, and ultimately make sense of incoming viewing data from millions of televisions.
As a member of this team, you will help architect, build, operate, and maintain our data pipelines responsible for aggregating television viewing data and deriving metrics and insights that power a variety of our data products and offerings. You will not only work on our pipeline jobs, but you will also have the opportunity to help build out and further evolve our internal frameworks upon which we process and deliver data at scale.
- Analyze and improve the efficiency, scalability, and stability of data collection, storage, and retrieval processes for our core systems.
- Create and manage platform-specific APIs.
- Create new data processing systems as necessary to support our Data Scientists and Research Analysts.
- Ultimately, build robust, high-volume production software.
- Strong command of a programming language or two – while we code primarily in Python, we acknowledge that engineers with sound fundamentals can pick up new languages relatively quickly.
- Excellent problem solving skills. Ability to interpret and analyze data is a must. Consequently, mathematical inclination is a major plus.
- 2+ years of professional development experience building high-performance, large-scale applications/pipelines.
- Solid foundation in computer science, with strong competencies in data structures, algorithms and software design.
- Experience with Hadoop, Spark, or similar technologies is desirable.
- Experience with running production systems on AWS is also a plus.
Post ID: 60
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