![]() ![]() Make the project scalable, because sooner or later, you will need to add in something new.Test-driven development is an important skill.Clients tend to change requirements as you go, especially if beforehand they didn't know exactly what they wanted.Requirements gathering and weekly client-update meetings are important.Our end-user base team consisted of about 40 users from data tracking and the report consumption. The project took us about three months to complete from inception through planning, development, QA, and release. The other developer was about 80/20 report developer and web developer. Web Developer Number 3 We had one main web developer whose job was to combine all of our work and integrate it into our already existing tools.I managed a team of 3 (myself included) developers to design and create a planning and budgeting tool for our clients. Running this group project was my largest accomplishment so far. Supported regularly the active users and produce additional functionalities as needed. ![]() Converted Python ETL to scheduled SSIS packages.Converted a MySQL database into an MS SQL database with use of triggers, stored procedures, views, and user-defined functions.Improved and stabilized a Python web application.Built a secondary MS SQL database keys and indexes.Implemented the integration of a pre-existing in-house web application.Learned MVC with C#, HTML5, Razor, Bootstrap, JavaScript, CSS, Lambda, and the Entity framework.Assisted with the development of an in-house web application dashboard and workflow.Created SSRS reports for c-level reporting and aggregation of data for end-user consumption.Utilized the functions and stored procedures for automated user group administration from LDAP. Completed the build of a MS SQL relational database keys and indexes.Worked multi-level to track projects, requests, bugs, and ad hoc tasks.Worked end-to-end on a ticket-tracking web application-planning, designing, development, and implementation.Scheduled SSIS packages for ETL from Oracle, Access, MySQL, and flat file sources.Correlated the data from 20+ data warehouses and inventory systems into a single database.Implemented query performance tuning (T-SQL, some PL/SQL and MySQL) and data cleansing/normalization.Educated power-users on the new data warehouse structure and access of data through Looker and DBeaver.Wrote documentation for the end-user and for the ease of knowledge transfer using a Confluence Wiki.Assisted in the logic construction for the finance and data analytics teams through the Looker BI Tool.Prepared prior to the launch of the new 2.0 website then configured and updated as new development occured.Built, improved, and maintained the Virtual Data Warehouse following the Kimball method using Dim and fact tables in a star schema style.Worked closely with Data Virtuality support while the new tool continued to grow.Became the resident expert of two new tools (Data Virtuality and DBeaver).Learned proprietary SQL language by Data Virtuality modeled similarly to PostgreSQL.Determined how to parse JSON for ingestion of GA data.Utilized Data Virtuality ETL for a compilation of multiple data sources including Amazon Redshift, NetSuite, PostgreSQL, Google Analytics (GA), Facebook, and CSV files. ![]()
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