Vacancy expired!
Sling TV L.L.C. provides an over-the-top (internet delivered) television experience on TVs, tablets, gaming consoles, computers, smartphones, smart TVs and other streaming devices. Distributed across a variety of strategic device partners, including Google, Amazon, Apple TV, Microsoft, Roku, Samsung, LG, Comcast, and many others, Sling TV offers two primary domestic streaming services that collectively include more than 100 channels of top content. Featured programmers include Disney/ESPN, NBC, AMC, A&E, EPIX, NFL Network, NBA TV, NHL Networks, Pac-12 Networks, Hallmark, Viacom, and more.
For Spanish-speaking customers, Sling Latino offers a suite of standalone and extra Spanish-programming packages tailored to the US Hispanic market. And for those seeking International content, Sling International currently provides more than 300 channels in 20 languages (available across multiple devices) to U.S. households.Sling TV is the #1 Live TV Streaming Service. Sling TV is a next-generation service that meets the entertainment needs of today's contemporary viewers. Visit www.Sling.com. We are driven by curiosity, pride, adventure, and a desire to win - it's in our DNA. We're looking for people with boundless energy, intelligence, and an overwhelming need to achieve to join our team as we embark on the next chapter of our story.Opportunity is here. We are Sling. What you'll be doing:This role will be responsible for expanding our data and data pipeline architecture, as well as optimizing data flow and collection for cross-functional teams. The ideal candidate is an experienced data pipeline builder and data wrangler, who enjoys optimizing data systems and building them from the ground up. This position will support our software developers, database architects, data analysts and data scientists on data initiatives and will ensure optimal data delivery architecture is consistent throughout ongoing projects. The successful candidate will:- Create and maintain optimal data pipeline architecture;
- Assemble large, complex data sets that meet both functional and non-functional business requirements;
- Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.;
- Build the infrastructure required for optimal extraction, transformation and loading of data from a wide variety of data sources using SQL and AWS 'big data' technologies;
- Build analytics tools that utilize the data pipeline to provide actionable insights into customer acquisition, operational efficiency and other key business performance metrics;
- Collaborate with stakeholders including the executive, product, data and design teams to assist with data-related technical issues and support their data infrastructure needs;
- Create data tools for analytics and data scientist team members that assist them in building and optimizing our product into an innovative industry leader;
- Work with data and analytics experts to strive for greater functionality in our data systems.
- A Bachelor's degree in Computer Science Engineering, Data Analytics, or a related technical degree.
- Five+ years of experience working with distributed data technologies (e.g. Hadoop, MapReduce, Spark, Kafka, Flink etc) for building efficient, large-scale 'big data' pipelines;
- Strong Software Engineering experience with proficiency in at least one of the following programming languages: Golang, Java, Python, Scala or equivalent;
- Implement data ingestion pipelines both real time and batch using best practices;
- Experience with building stream-processing applications using Apache Flink, Kafka Streams or others;
- Experience with Cloud Computing platforms like Amazon AWS, Google Cloud etc.;
- Experience supporting and working with cross-functional teams in a dynamic environment;
- Experience with relational SQL and NoSQL databases, including Postgres and Cassandra.
- Experience with ELK stack.
- Ability to work in a Linux environment.
- Experience in building distributed, high-volume data services;
- Experience with big data processing and analytics stack in AWS: EMR, S3, EC2, Athena, Kinesis, Lambda, Quicksight etc.;
- Knowledge of data science tools and their integration with data lakes;
- Experience in container technologies like Docker/Kubernetes
Vacancy expired!