Senior Data Engineer 5

Data Science and Engineering (DSE) is a key pillar of Netflix’s data-driven strategy that supports decision-making with data, analytics, experimentation, models, and consumer insights. The Data Engineering group focuses on building efficient data-processing systems and modelling data for analytics, ranging from batch pipelines powering business metrics to real-time services supporting core product features. They work with large distributed systems and collaborate closely with business, engineering, and data science teams.

• 6+ years of experience building data pipelines in batch and/or real-time settings.
• Proficiency in Python and/or Scala for scripting, automation, and data orchestration frameworks.
• Ability to write complex SQL for ad-hoc and recurring workflows.
• Experience with Spark, Flink, Kafka, and Iceberg is helpful but not strictly required.
• Understanding of data modelling for efficient reporting and metrics, organizing data for scale and fast retrieval.
• Experience sourcing and modelling data from application APIs and event streams.
• Ability to convert business requirements into data engineering workstreams.
• Experience mentoring and unblocking team members.
• Strong collaboration skills across functions including marketing, data science, and product management.

Nice to have:

• Experience with Scala specifically.

• Fully own critical pipelines and data sets to support a variety of use cases in data science and engineering.
• Directly collaborate with stakeholders to understand needs, model tables using data warehouse best practices, and develop data pipelines to ensure timely delivery of high-quality data.
• Translate ambiguous business questions into clear data requirements and deliver on them.
• Serve as a bridge between data engineering and the business, enabling insights that empower colleagues to make informed decisions.
• Build strong partnerships with data scientists, analytics engineers, and machine learning practitioners to enable research and insight delivery.
• Work with productivity leaders and product managers to turn business requirements into data engineering projects, track and analyze metrics, and address data quality issues.
• Architect pipelines ingesting data from multiple sources to support marketing, fandom, security, and membership data engineering teams.
• Mentor and guide other team members, navigate ambiguity, and create clarity.