Senior Data Engineer

NBCUniversal•New York, NY

18h•$115,000 - $145,000•Hybrid

About The Position

The Media Group at NBCU supports a powerhouse collection of consumer-first brands including Peacock, NBC, Bravo, NBC Sports, and NBCU International. With unequalled scale, our teams make the most out of every opportunity to collaborate and learn from one another. We’re always looking for ways to innovate faster, accelerate our growth and consistently offer the very best in consumer experience. But most of all, we’re backed by a culture of respect. We embrace authenticity and inspire people to thrive. As part of the Media Group Decision Sciences team, the Senior Data Engineer will be responsible for creating a connected data ecosystem that unleashes the power of our streaming data. We gather data from across all customer/prospect journeys in near real-time, to allow fast feedback loops across territories; combined with our strategic data platform, this data ecosystem is at the core of being able to make intelligent customer and business decisions. In this role, the Senior Data Engineer will share responsibilities in the development and maintenance of data pipelines that facilitate data driven machine learning, as well as support ongoing operations related to the Media Group data ecosystem.

Requirements

3+ years relevant experience in Data Engineering
Programming skills in one or more of the following: Python, Java, Scala
Proven ability to write reusable/efficient code to automate analysis and data processes
Intermediate level of experience querying relational databases with SQL
Demonstrated experience implementing scalable, distributed, and highly available systems in a cloud ecosystem
Ability to work with graph-based data workflows (e.g., Apache Airflow)
Ability to develop data catalogs while ensuring clarity and correctness of key business metrics
Experience building streaming data pipelines using Kafka, Spark or Flink
Data modelling experience (operationalizing data science models/products a plus)
Bachelor’s degree with a specialization in Computer Science, Engineering, Physics, other quantitative fields or equivalent industry experience.

Nice To Haves

Experience in processing structured and unstructured data into a form suitable for analysis and reporting with integration with a variety of data metric providers ranging from advertising, web analytics, and consumer devices
Experience building and deploying ML pipelines: training models, feature development, regression testing
Strong Test-Driven Development background, with understanding of levels of testing required to continuously deliver value to production.
Experience with large-scale data for analytics (media experience a plus)
Ability to work effectively across functions, disciplines, and levels
Team-oriented and collaborative approach with a demonstrated aptitude, enthusiasm and willingness to learn new methods, tools, practices and skills
Ability to surface and navigate differing perspectives constructively to drive alignment and sound technical decisions
Pride and ownership in your work and confident representation of your team to other parts of NBCUniversal

Responsibilities

Collaborate with a high-performance team of Data Engineers
Contribute to and facilitate design, build, testing, scaling, and maintaining data pipelines from a variety of source systems and streams (Internal, third party, cloud based, etc.), according to business and technical requirements.
Deliver observable, reliable and secure software, embracing “you build it you run it” mentality, and focus on automation and GitOps.
Continually work on improving the codebase and have active participation and oversight in all aspects of the team, including agile ceremonies.
Take an active role in story definition, assisting business stakeholders with acceptance criteria.
Work with Principal Engineers to share and contribute to the broader technical vision.
Develop and champion best practices, striving towards excellence and raising the bar within the department.
Develop solutions combining data blending, profiling, mining, statistical analysis, and machine learning, to better define and curate models, test hypothesis, and deliver key insights
Operationalize data processing systems