Greg Martinez

Data and cloud engineer with a passion for good documentation.

Where I come from

I got my B.S. in Chemical Engineering in 2017 from the University of California, Irvine. I worked for a couple of years at JLL. We were contracted to a biomedical company called BMS. I started as a lab technician – I cleaned out biomedical waste, washed and sanitized glassware, and helped manage the store room. I started learning VBA and built an Excel dashboard to show that our new Point of Use service was increasing the researchers’ time in the lab. I was promoted to Reagent Manager and then Maintance Planner/Scheduler on a career path towards reliability engineering. I taught myself more and more software, moving on to Python and other languages.

I moved on to Honeywell-Intelligrated as a CMMS (Computer Maintenance/Management Service) administrator. There my job evolved into BI specialist then data engineer. I pioneered automated data pipelines that were used to build standardized reports for several different customers. The previous method had been manual extractions from the CMMS’s online SQL interface. I used Azure functions to ETL the raw, unstructured data from the CMMS and Azure Data Factory to save them to our Azure SQL database.

I later joined an online education company called ABC Learning. I was a software developer/data engineer working mostly in PHP and Snowflake. This was my first experience with a mature CI/CD pipeline and proper software development practices, like code reviews. The pace of the job was too slow for me; I needed something that could provide a creative outlet.

I came to Milliman looking for more opportunities to explore and learn. I dove into Databricks and cloud computing. I’m learning about how Databricks manages clusters and how it connects to external storage services, like S3. I’m learning how AWS manages roles and permissions and user identity. I’m learning how to work with cutting-edge technology, like OpenAI. I’m learning how to switch from support engineer to data engineer to AI engineer to technical writer and back again. I plan to keep learning as much as I can.

What I like to do

I like to read. I read a lot of BFF (Big Fat Fantasy) novels. I like books that discuss attention and how our attention has been affected by the digital age. I read books about books and books on reading (The Library, The Art of Libromancy, In Praise of Good Bookstores, The Book, The Manuscript Club, The Library Book, The Devil’s Library, etc). The book, as an object, is fascinating and is saturated in history and meaning.

I like to cook. I like Mexican-style food, like enchiladas or tacos. I like to make salsas and sauces. I even make my own tortillas.

I like to write as well. I like to get my ideas across to people. Writing is one of the few ways that I can expresss my creative urges. Even if what I write from day to day is very straightforward and fact-heavy. The push and pull of getting the balance of a sentence just right; the struggle to find the right word; the joy when someone references your work and can find the answer they were looking for. I find the craft very satisfying.

What I do for work

My title is “data engineer”, but I wear many hats.

I’ve done plenty of data engineering. I’ve created pipelines for our recent Unity migration and helped practices transfer large amounts of data from one account to another.

But I do much more than just pipelines. I’ve helped support users with their cloud infrastructure. I’ve helped the Knowledge Managemnt team with MCP documentation and myriad other topics. I’ve helped explore our AI use cases in OpenAI and in Databricks.

I would like to focus more on technical writing. I have plans to work on some more detailed documentation for working in Databricks. I also have plans to extend our AI offerings both through Azure OpenAI and Databricks’s model.