Speakers

Elementl leaders are frequent presenters at conferences and guests on podcasts.
If you would like to book one of the following speakers, please contact us.
Nick Schrock

Nick Schrock

Nick is the founder of Elementl, the startup behind Dagster, a popular open-source, data orchestration platform. Originally its CEO, Nick now serves as its CTO and Chairman. Prior to Elementl he was a Principal Engineer and Director at Facebook from 2009 to 2017, where he founded the Product Infrastructure team and co-created GraphQL.

Topics

  • The present and future of data engineering
  • Data engineering best practices: building engineering tools for engineers
  • The Engineering Founder’s dilemma: to lead the vision or run the company?
  • The operational convergence of ML, AI and data engineering
  • The delicate balance of an open-core business model

Prior appearances

Pete Hunt

Pete Hunt

Pete joined Elementl as head of engineering in early 2022, and took over the reins as CEO in November of that year. Pete was previously co-founder and CEO of Smyte, an anti-abuse provider that was acquired by Twitter. Prior to his Pete led Instagram’s web team, built Instagram’s business analytics products, and helped to open source Facebook’s React.js.

Topics

  • Building and leading a data engineering team
  • Engineering hard choices: selecting an app’s tech stack
  • The transition from engineering to CEO (and back, then back again)
  • Building out a commercial cloud offering from an open-source base

Prior appearances

Sandy Ryza

Sandy Ryza

Sandy is a lead engineer, author, and thought leader in the domain of data engineering. Sandy co-wrote “Advanced Analytics with PySpark” and "Advanced Analytics with Spark”. He led ML and data science teams at Cloudera, Remix, Clover Health, and KeepTruckin.

Sandy is currently the lead engineer on the Dagster project, an open-source data orchestration platform used in MLOps, data science, IOT and analytics. Sandy is a regular speaker at data engineering and ML conferences.

Topics

  • Data engineering in a post-Hadoop world
  • The move to declarative and functional programming in data engineering
  • The convergence of ML, data science, and data engineering
  • The role of data engineering in the modern enterprise

Prior appearances

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