Data Analyst

Datacast Episode 105: Building The Next-Generation Spreadsheet, Being A Curious Analyst, and Engineering Entrepreneurship with Bobby Pinero

Datacast Episode 105: Building The Next-Generation Spreadsheet, Being A Curious Analyst, and Engineering Entrepreneurship with Bobby Pinero

Bobby Pinero is the CEO and Co-Founder of Equals, a next-generation spreadsheet. He has worked in Finance, Analytics, and Data generally for the last few years. Previously he was the head of Finance and early employee number 8 at Intercom.

Datacast Episode 92: Analytics Engineering, Locally Optimistic, and Marketing-Mix Modeling with Michael Kaminsky

Datacast Episode 92: Analytics Engineering, Locally Optimistic, and Marketing-Mix Modeling with Michael Kaminsky

Michael Kaminsky is the co-founder of Recast, a marketing optimization platform, and the co-founder of Analytics Engineers Club, a training course for data analysts looking to improve their engineering skills. He is passionate about helping organizations “make better decisions faster.” He has experience applying econometric research methods to environmental economics, child welfare policy, and medical treatment efficacy. He studies Spanish, reads, and pets dogs around Mexico City in his spare time.

What I Learned From The Modern Data Stack Conference 2021

What I Learned From The Modern Data Stack Conference 2021

Back in September 2021, I attended the second annual Modern Data Stack Conference, Fivetran’s community-focused event that brings together hundreds of data analysts, data engineers, and data leaders to share the impact and experiences of next-generation analytics. The presenters shared the transformations they experienced with their analytics teams, the new insights and tooling they enabled, and the best practices they employ to drive insights across their organizations.

In this long-form blog recap, I will dissect content from 14 sessions that I found most useful from the conference. These talks are broken down into 4 categories tailored to 4 personas: data engineers, data analysts, product managers, and data team leads. Let’s dive in!