Emily Riederer
About
I'm a long-time dbt user and have been an active community member for a few years.
Professionally, I've led a variety of data teams at Capital One spanning analytics, modeling, innersource data tools, and data infrastructure. The common denominator of all of these roles has been the overwhelming importance of high quality data processing pipelines. Outside of work, I enjoy doing pro bono projects and applying my same skillset to scrappier environments.
My work with the dbt community is motivated by a passion for data quality and developer tooling. Some of my recent contributions include maintaining the dbtplyr package, speaking at Coalesce 2021, and writing a dbt Developer Blog post about my PR to the dbt-utils test suite.
When did you join the dbt community and in what way has it impacted your career?
I have been involved in the dbt community for a few years now. While I enjoy being actively engaged, one of my favorite parts is simply "lurking" on the Slack channels. The data space is moving so fast right now with so many different competing frameworks, tools, and ideas. At the same time, data work tends to be less discussed and publicly shared than analysis methods (e.g. new modeling packages) due to data privacy and IP. I've found no better place to "drink from the firehouse" and benefit from the insights of others questions, challenges, and successes.
What dbt community leader do you identify with? How are you looking to grow your leadership in the dbt community?
Two community members that really inspire me are Claire Carroll and Joel Labes. I think both showcase excellence in technical best practices, crystal-clear communication of technical concepts in their prolific writing, and a passion for building community and creating on-ramps. That mix of so-called 'hard' and 'soft' skills adds so much to the community and helps empower every member to be their best. I'm always looking to balance the time I spend growing my skills along both dimensions.
What have you learned from community members? What do you hope others can learn from you?
Given my passion for data quality and the design side of data, I particularly enjoy thinking about data modeling and learning from the community's experience with the variety of classical and novel frameworks for designing resilient, flexible datamarts.
As a passionate fan of open-source (and the also-thriving #rstats community), I hope to inspire others to create more packages and PRs that expand the developer toolkit. I also particularly enjoy discussing my thoughts on data quality and avoiding data disasters.
Anything else interesting you want to tell us?
test My passion for open-source tools and open-knowledge extends beyond dbt. I also enjoy serving on the editorial board for rOpenSci to champion the creation of open-source research software, reviewing technical books for CRC Press, doing pro-bono data projects, and sharing my own learnings through conference talks and writing (including on my website, guest blogs, and books including R Markdown Cookbook and 97 Things Every Data Engineer Should Know).