Episode 91: Collaborative Data Workspace, The Sharing Gap, and Engineering Management with Caitlin Colgrove

Show Notes(01:37) Caitlin went over her college experience studying Computer Science at Stanford University in the early 2010s.(03:55) Caitlin talked about her teaching experience for CS 106A and CS 103.(07:09) Caitlin shared valuable lessons from completing software engineering internships at Harvard University, Facebook, and Palantir.(10:06) Caitlin walked over technical and organizational challenges during her time at Palantir — building products for both government/commercial customers and working with designers/infrastructure engineers to deliver full-stack applications to the field.(12:01) Caitlin explained why Palantir is composed of “loosely individual startups.”(14:56) Caitlin recalled learning curves during her transition to a tech lead role at Palantir — becoming responsible for the technical architecture and code quality of the product, mentorship and growth of the engineers, and the product direction and prioritization of features.(18:31) Caitlin discussed her time as a Data Engineering Manager at Remix Technologies — leading the team that builds geospatial data pipelines on top of AWS, Postgres/PostGIS, and Apache Airflow.(24:45) Caitlin reflected on valuable leadership and people management lessons absorbed during her transition to growing and developing diverse and inclusive engineering teams.(29:05) Caitlin shared the founding story of Hex, the modern data workspace for teams, alongside her co-founders Barry and Glen.(32:58) Caitlin talked about Hex’s ideal users (the “analytically technical” who need better tools to access and manage more sophisticated workflows) and introduced Hex’s Logic View.(35:22) Caitlin examined the collaboration challenges in data teams and revealed Hex’s Library to address some of the shortcomings.(39:59) Caitlin shared her thoughts on the evolution of data science notebooks.(42:14) Caitlin unpacked the nuanced problem of justifying data ROI to functional stakeholders and described Hex’s interactive App Builder.(45:17) Caitlin shared exciting development in the horizon of Hex’s product roadmap.(46:37) Caitlin shared valuable hiring lessons to attract the right people who are excited about Hex’s mission.(52:10) Caitlin shared the hurdles to find the early design partners and lighthouse customers of Hex.(56:01) Caitlin shared upcoming go-to-market initiatives that she’s most excited about for Hex.(58:24) Caitlin shared fundraising advice for founders currently seeking the right investors for their startups.(01:01:42) Closing segment.Caitlin’s Contact InfoLinkedInTwitterHex’s ResourcesWebsite | Twitter | LinkedInLogic View | App Builder | Knowledge LibraryDocs | Blog | GalleryCustomers | Careers | Integrations | PricingMentioned ContentArticles“Long Live Code” (June 2020)“Don’t Tell Your Data Team’s ROI Story” (Aug 2020)“The Sharing Gap” (Oct 2020)PeopleTristan Handy (Founder and CEO of dbt Labs)Claire Carroll (Product Manager of Hex, previous Community Manager of dbt Labs)Wes McKinney (Creator of Pandas and Arrow, Co-Founder and CTO of Voltron Data)DeVaris Brown (Co-Founder and CEO of Meroxa)Book“Mindset: The New Psychology of Success” (by Carol Dweck)Notes

My conversation with Caitlin was recorded back in Fall 2021. Since then, many things have happened at Hex. I’d recommend looking at:

Caitlin’s piece announcing Hex’s SOC 2 Type II report to reflect Hex’s commitment to securityCaitlin’s recent talk at Data Council Austin about implementing reactive notebooks with iPythonThe release of Hex Knowledge Library, a new way to publish and discover data workHex’s $16M Series A (led by Redpoint Ventures) and $52M Series B (led by a16z along with Snowflake, Databricks, and existing investors)Hex’s increasing list of customers such as AngelList, Fivetran, Hightouch, Loom, Mixpanel, Notion, Ramp, Replicated, SeatGeek, etc.

About the show

Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths — from scientists and analysts to founders and investors — to analyze the case for using data in the real world and extract their mental models (“the WHY and the HOW”) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.

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