Cloud Data Engineer, Revenue Science

New York, NY

Who We Are:


As data engineers in Revenue Science, our mission is to build real-time and offline solutions to make data accessible and reliable while leveraging the largest-scale data processing technologies in the world -- and then apply them to the Revenue’s most critical and fundamental data problems.


Learn more about some of the challenges we tackle on this team:


What You’ll Do:


As a member of the Data Engineering team, you will build and own mission-critical data pipelines that are ‘source of truth’ for Twitter’s fundamental revenue data, as well as modern data warehouse solutions, while collaborating closely with Ads Data Science team.


You will be a part of an early stage team and have a significant stake in defining its future with a considerable potential to impact all of Twitter’s revenue and hundreds of millions of users.


You will be among the earliest adopters of bleeding-edge data technologies, working directly with Revenue Science and Revenue Platforms teams to integrate your services at scale.


Your efforts will reveal invaluable business and user insights, leveraging vast amounts of Twitter revenue data to fuel numerous Revenue teams including Ads Analytics, Ads Experience, Ads Data Science, Marketplace, Targeting, Prediction, and many others.



Who You Are:


You are passionate about data and driven to take the data organization challenges at the scope of entire Twitter’s Revenue.


What you’ll need:

  • Strong programming and algorithmic skills
  • Experience with data processing (such as Hadoop, Spark, Pig, Hive, MapReduce etc).
  • Proficiency with SQL (Relational, Redshift, Hive, Presto, Vertica)


Nice to have:

  • Experience writing Big Data pipelines, as well as custom or structured ETL, implementation and maintenance
  • Experience with large-scale data warehousing architecture and data modeling
  • Proficiency with Java, Scala, or Python
  • Experience with GCP (BigQuery, BigTable, DataFlow)
  • Experience with Druid or Apache Flink
  • Experience with real-time streaming (Apache Kafka, Apache Beam, Heron, Spark Streaming)
  • Ability in managing and communicating data warehouse project plans to internal clients



Engineering Hiring Process

Step 1

Once your application is received, a recruiter will reach out pending your qualifications are a match for the role.

Step 2

If your background is a match, you may have 1-2 technical phone interviews or be given the chance to provide a work sample depending on the role.

Step 3

If the phone interviews go well or your work sample is strong, the final step includes interviews with 5-6 people held onsite in our office.

Application

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