The Video Chief Data Office organization is looking for a curious, talented and highly motivated data engineer to work closely with data analysts and business users as part of an agile team to develop datasets that enable data visualization, machine learning and data driven decision making across our streaming and satellite TV products.

You will be comfortable working with structured and unstructured datasets and automating data pipelines to ingest, analyze, validate, normalize and clean data in a hybrid multi-cloud environment as well as data modeling, quality and governance.

This is the perfect opportunity to become a part of a fast paced and innovative team that leverages data, reports, dashboard and machine learning to solve real world problems and drive business value.

Key Roles and Responsibilities

• Gathers, analyzes, and interprets a wide variety of data utilizing a variety of statistical methods to support Data Analysts and Business Users.
• Automates data pipelines to ingest, analyze, validate, normalize and clean data.
• Data Quality Implements statistical data quality procedures on new data sources.
• Develops and maintains data engineering best practices.
• Provides technical and thought leadership and mentoring to junior members of the team.
• Works with Security teams and Legal to create data policies and develop interfaces and retention models which requires synthesizing or anonymizing data.
• Develops basic reports, charts, tables, and other visual aids in support of findings.
• Advises business partners with regards to patterns and relationships in data to recommend business direction or outcomes.

Experience

• Preferred: SQL, Snowflake, Vertica, Teradata, AWS and Azure.
• Bonus: Python, Databricks (Spark), Hortonworks
• Good understanding of internal business segments or stakeholders and good presentation skills.
• 6-8 years related experience in data engineering/automation, relational database structures, data modeling and data quality.

Education

Preferred advanced college degree or masters in Computer Science and or Statistics, Mathematics.