Job Description
Senior-Big Data Engineer in Dallas, TX
Location: Dallas Texas United States
AT&T Services, Inc. is looking for a Senior-Big Data Engineer in Dallas, TX [and various unanticipated locations throughout the U.S.; may work from home] to interpret the requirements of various big data analytic use cases and scenarios. Drive the design and implementation of specific data models in order to drive better business decisions through insights from data assets.
- Develop enablers and data platforms in big data lake environments and maintain integrity during the life cycle phases.
- Define data requirements, gather and mine large scale of structured and unstructured data, and validate data by running various data tools in a big data environment.
- Support standard, customized, and ad-hoc data analysis.
- Develop mechanisms to ingest, analyze, validate, normalize, and clean data. Implement statistical data quality procedures on new data sources.
- Apply rigorous iterative data analytics. Support data scientists in data sourcing and preparation in order to visualize data and synthesize insights of commercial value.
- Work with big data policy, security teams, and legal teams to create data policy.
- Develop interfaces and retention models that require synthesizing or anonymizing data.
- Develop and maintain data engineering best practices.
- Provide insights on data analytics, visualization concepts, methods, and techniques.
- Build and maintain BI, ML, and AI platforms that support users across AT&T.
- Apply advanced knowledge of cloud based tools, vendor tools, and related technologies including Azure Cloud.
Requires:
- a Master s degree, or foreign equivalent degree in Computer Science, Computer Engineering, or Electrical Engineering and two (2) years of experience in the job offered
- or two (2) years of experience developing enablers and data platforms in big data lake environments and maintaining integrity during the life cycle phases; supporting standard, customized, and ad-hoc data analysis; developing mechanisms to ingest, analyze, validate, normalize, and clean data; developing interfaces and retention models that require synthesizing or anonymizing data; providing insights on data analytics, visualization concepts, methods, and techniques; and applying advanced knowledge of cloud based tools, vendor tools, and related technologies including Azure Cloud.