Location: This opportunity is available remote, however, the majority of the team sits in Denver.
The Data team is an elite team of data and marketing strategy AND technology specialists with expertise in the ever-changing universe of precision marketing, ad-technologies, and data architecture and management.
The Data Engineer helps to define, execute, and support the delivery of the technical data architecture for Entercom's data team. The work will improve the quality, reliability, accuracy, and consistency of our data by engineering project-specific data pipelines and validation tools, and the team's overall data model. The candidate will have a hands-on role with product specialists and be responsible for working with them to understand data requirements and then execute by implementing those requirements. As part of the Data Team, you will help build data pipelines that empower the organization's ability to make more informed decisions.
The successful candidate will have a fully-rounded knowledge of successfully developing and delivering organizational and client value solutions.
• Collaborate with product teams, data analysts and data scientists to help design and build data-forward solutions
• Design, build and deploy streaming and batch data pipelines capable of processing and storing petabytes of data quickly and reliably
• Integrate with a variety of data metrics providers ranging from advertising, mobile/web analytics, and consumer devices
• Build and maintain dimensional data warehouses in support of business intelligence and optimization product tools
• Develop data catalogs and data validations to ensure clarity and correctness of key business metrics
• Drive and maintain a culture of quality, innovation and experimentation
• Coach data engineers best practices and technical concepts of building large scale data platforms
• 3-5 years of experience developing in object oriented Python
• Experience deploying and running AWS based data solutions and familiar with tools such as Cloud Formation, IAM, Athena, Redshift, and Kinesis
• Experience engineering big-data solutions using technologies like Databricks or EMR, S3, Spark and an in-depth understanding of data partitioning and sharding techniques
• Familiar with metadata management, data lineage, and principles of data governance
• Experience loading and Querying cloud-hosted databases such as Redshift and Snowflake
• Building streaming data pipelines using Kafka, Spark, or Flink
• Familiarity with binary data serialization formats such as Parquet, Avro, and Thrift
• Experience deploying data notebooks and analytic environments such as Jupyter and Databricks
• Knowledge of the Python data ecosystem using pandas and numpy
• Experience building and deploying ML pipelines: training models, feature development, data integrations, regression testing
• Experience with graph-based and event based data workflows using Apache Airflow, AWS Lambda, Docker, and/or Kubernetes
• Bachelor's degree in Computer Science or a related field or equivalent work experience