Nicole Wigston

Certified Quality Foods developed a unique device to objectively assess food quality via a device inserted into a food sample, such as a filet of fish. Our team of remote contractors developed the mobile application interfacing with the device (via Bluetooth) to report measurements in the form of charts, graphs, and a mapping component.

As a scrum master, I facilitated daily scrum, planning, review, and coordination of communication via Slack for the team, consisting of three developers, a designer, and a product owner. The five team members were from four different companies, and I was contracted as a freelancer.

Acting as a project manager, I finessed relationships between our development team, the CQ Foods investors and stakeholders, and integration with the hardware and database development teams. I tracked hours, budget, risks, issues, and built details in weekly status meetings with key stakeholders.

A department director asked me to lead a project to automate the collection of remote sensing flight operations data. Each stakeholder I spoke to had a different vision of success. By conducting interviews to elicit more clear requirements, I was able to articulate the project vision and present a proposal to the C suite.

For the proposal, I identified the dev stack, the budget, staff needs, and an implementation timeline. After my presentation, I was commended by several senior leaders for finally articulating the project vision in concrete and realistic terms.

The project team consisted of a front-end developer (JavaScript), a back-end developer (Flask/Python), a newly hired DevOps engineer, a database designer, and a product owner. The team was not dedicated, so I regularly encouraged each team member to take on manageable tasks outside their specialty.

We implemented a database schema in PostgreSQL, developed a web application that parses source files to populate tables, built a DevOps pipeline to automate deployments across test, dev, and prod instances, and deployed this solution to thirty staff members across four roles with change management and training.

The MVP took just six months to develop and deploy.

I conducted a needs analysis, including observing the staff’s current workflows utilizing various data sources to make future site capacity planning recommendations. Along with the GIS analyst on the team, I identified Esri’s Web App Builder deployed on AWS along with ArcGIS Enterprise (a full-stack solution) as the software solution.

The project team included a GIS analyst, a server admin, a database analyst, and a product owner who had not previously been part of a scrum team. I socialized the scrum ceremonies and promoted cross-functional participation. There was resistance to daily scrum meetings initially, which I phased in by socializing an agreement to communicate daily at 9 AM via Slack. The team eventually recognized in a retrospective the value of daily communication.

As the project got underway, we identified the need for further custom JavaScript development and brought on a subcontractor to fill that need. Our team identified the option to purchase curated demographic data that resulted in a labor cost savings of $22,000. We maintained development, test, and “demo” environments (for the functionality presentation to stakeholders).

Federal agencies commonly need to report at a national level, but data is maintained at a regional office level. It is challenging to aggregate hundreds of datasets, each with its own schema definitions, to a meaningful national dataset. This project listed sixty datasets necessary for reporting at a national level.

I led a team of business analysts and GIS data experts to determine which datasets were offered as existing web services that would suit the business needs, then determine which datasets would need to be internally sourced.

For the internal data, we identified gates each dataset would progress through (for each of 11 participating states). We used Kanban boards in Azure DevOps for each dataset and each state to track progress in identifying available data, assessing its quality and conformity to a national standard, preprocessing, performing ETL for migration into a SQL Server geodatabase, then creating web services for that data.