Senior Developer Advocate - Data Observability

Our Senior Developer Advocates are technical leaders and mentors that anchor our team. They own projects from beginning to end, facilitating collaboration and enabling Datadog's community to solve real-world problems. With a focus on Data Observability, this role will enable our community of engineers around Datadog to be part of a movement of building better software. This is a unique opportunity to use both your engineering expertise and advocacy skills to shape the ever-evolving technological landscape.

What You’ll Do:

• Act as a subject matter expert for data observability on behalf of the Datadog advocacy and engineering teams
• Create content in one or more mediums to build Datadog's reputation as a leader in data engineering and observability e.g. building demos, public speaking, blogging, documentation, webinars, open source, research reports and more
• Partner with and coach internal product and customer engineering teams on effective public communication and presentations for the work they do
• Contribute to the product through feedback (bugs or product enhancements suggestions)
• Identify and pursue opportunities for events, programs, and other community-focused work that establishes trust with practitioners

Who You Are: 

You are a trusted technical expert who enjoys helping data practitioners understand, operate, and improve complex data systems in production. You bring deep, hands-on experience in one or more areas of modern data engineering, and you use that experience to provide clear context to both the community and internal teams.

You are not expected to be an expert in every area below. Instead, you bring depth in some and working familiarity across many, and you are comfortable connecting them into a coherent operational story.

• Data engineering & processing systems: You have built, operated, or supported production data pipelines using distributed processing systems such as Apache Spark or Databricks, and understand common failure modes, performance tradeoffs, and operational challenges in batch and hybrid pipelines.
• Data platforms & analytics systems: You have hands-on experience with analytics platforms such as Snowflake and BigQuery, including schema design, data modeling, SQL-based analysis, and reasoning about performance, cost, and access patterns in real-world environments.
• Streaming & event-driven data: You understand how data flows through streaming systems such as Kafka or similar platforms, including producer and consumer behavior, lag, delivery semantics, and how streaming issues propagate into downstream datasets and analytics.
• Data quality, lineage, and metadata concepts: You are familiar with how data teams reason about data quality, upstream and downstream impac