Sr. Analytics Engineer, Twitter Service
Who We Are:
The teams in Twitter Services (TwS) department are on the front lines creating the best possible experience for our global users as well as excellent support experience through both direct interactions and scalable solutions.
Twitter users generate many terabytes of data every day; The TwS analytics team is at the intersection of all this data and strives to make it actionable to all business units within TwS. Analytics team build real-time and offline solutions to make data accessible and reliable -- and then apply them to the most critical and fundamental analytical problems to guide TwS business decisions via observational analyses, trend analyses, modeling, and new measurement strategies.
What You’ll Do:
We are trying to improve Twitter Service. To improve something, we need to be able to measure it. As an analytics engineer, you will enable better measurements and ensure measurement accuracy so that we know where we are doing well and where we want to improve.
You’ll work closely with engineering and business stakeholders, to design and build analytics platform from distributed and diverse data sets, that is a ‘source of truth’ for Twitter service users.
- Write complex data flows using SQL (Vertica, Presto, BigQuery), Scala, R or Python scripts to move data and to provide intuitive analytics
- Use data visualization tools (e.g, Tableau, Zeppelin) to translate data patterns and insights into recommended areas for service improvement
- Apply qualitative and quantitative techniques to model user behavior, identify causal impact and attribution, build and benchmark metrics
- Create and enforce safeguards that limit and track internal access to TwS data
- Improve the data extraction pipeline through data mapping, validation, reconciliation, cleansing, standardization, and integration
- Actively track data quality issues, propose mitigations, and document data proxies
- Audit data sources, understand data dependencies, identify data gaps, and propose changes to extend the scope of analytics platform
Who You Are:
You love writing code and have an expertise in some or all of the following:
- Data Warehousing
- Metrics Development
- Predictive / Descriptive modeling
- We follow agile framework and processes. Hence, cross-functional collaboration, communication skills and a focus on delivering a great user experience are a must
- You should be comfortable managing work plan, timelines and milestones
- Ability to provide actionable feedback to support a high performing team while encouraging expression of opposing views to build trust
- SQL proficiency! Experience with big data tools such as Hadoop, BigQuery, Presto, Vertica, Spark, etc.
- Strong programming skills with Scala. ETL implementation experience
- Experience using data visualization tools (Tableau, Zeppelin) for story telling
- Understanding of and experience using analytical concepts and statistical techniques: hypothesis development, designing tests/experiments, analyzing data, drawing conclusions, and developing actionable recommendations for business units
- You have a sense of urgency, move quickly and ship things
- B.S. and/or M.S. in Computer Science or a related technical field, or equivalent experience.
- You're experienced in metrics and experiment-driven development
- Statistics background
- Proficiency in building predictive / descriptive models in R or Python is desired but not required
Note: Potential exposure to sensitive or graphic content, including but not limited to vulgar language, violent threats, pornography, and other graphic images.
You will see a direct link between your work, TwS growth, and user satisfaction.
Applicants will be considered for this role at all levels depending on qualifications. You will report to Sr. Manager of Analytics in San Francisco. This is a full-time position.
After you apply, a recruiter may reach out to you for an introductory call.
If your background is a match for the role, you may phone interview with 1-2 people.
If you continue through the process, you will come onsite 1-2 times to interview with a total of 5-10 people.
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