Suggestions
Laura M.
Data @ Morning Brew
Laura M. is a Principal Analytics Consultant with a Master of Science in Data Analytics, specializing in helping executives and data teams define and execute data strategies.
With a strong focus on a business-first approach, Laura works with companies to identify their business needs and priorities, implementing technology solutions to meet those requirements.
She leads consulting teams and client teams through various data projects including data pipeline migrations, cloud implementations, and data analytics using modern data tools.
Laura is proficient in GCP, Snowflake, Airflow, dbt, Fivetran, Looker, Tableau, Power BI, SQL, Python, R, Excel, and BI tools, leveraging SME knowledge for deep analysis to drive business strategies forward.
Her background includes roles as a Data Analyst and Data Engineer, specializing in ETL/ELT, data pipeline management, BI implementation, and KPI/metric tracking.
Laura's education includes a Master of Science in Business Analytics from the University of Colorado Boulder, a Bachelor of Science in Business Administration from the University of Wyoming, and a student exchange program at the University of Stirling.
She has held positions at prestigious organizations such as Lead Engineer, Data & Analytics at Morning Brew, Principal Consultant at DAS42, Consulting Team Lead at DAS42, and Associate Analytics Consultant at DAS42.
Her earlier roles include Data Analyst Intern at Jacobs, Title Analyst II at Anadarko Petroleum, Land Administration Intern at Anadarko Petroleum, and Public Service Commission Analyst at the State of Wyoming.
Highlights
Last month, I attended NRF 2023 with some of my peers from DAS42 to engage with leaders from the Retail industry. Learning and talking to some of the best and brightest in the industry, I recapped a few of the top themes in Retail for 2023 in a new post: http://ow.ly/8VhW50MIyrj
#retail #retailindustry #tech #data
One of my favorite parts of my job is working with brilliant Data People to build solutions that are both elegant and resilient; data-centric solutions to help drive businesses forward. However, too often these teams are plagued by the "just throw a tool at it" solution, which can lead to sprawling tech stacks. I wrote about this problem in a recent article in VentureBeat, where I share my approach to a technology rationalization framework (and an adage from the great Alton Brown) in the hopes of helping data teams build great things, faster, and without the bulk.
