Paul Butler

I served as a data architect, designing the data model and algorithm to match potentially duplicate consumer data and merge them into unique individuals. This used a nearest neighbors methodology to determine that multiple candidate consumer data records from multiple sources (such as lead management or vehicle registration processes) are, in fact, single individuals. I used Snowflake, dbt, and Python for implementation and coded aspects of this application while serving as lead for the overall implementation.

As a result, the Snowflake analytics platform is the single source of truth regarding consumer data, integrating many sources and facilitating the assembly of data for a customer data platform (Tealium).

I implemented a series of data integration applications to share data from the enterprise analytics platform (Snowflake) with customs processing partners. This facilitated the shipping/delivery of all BRP products through a variety of borders around the world. Specific data needed to be pre-processed following highly complex business rules and transformed into formats suitable for sharing via web APIs to the customs partners. I designed and built a system on Snowflake to log and monitor the flow of data through the preparation and delivery process for all products. I also designed and delivered a large amount of Snowflake SQL views via Dbt projects.

I designed algorithms to properly identify incremental data sets and conditions to resend information per a variety of business rules.

I evaluated cloud-based analytic platforms and chose Snowflake as the new self-serve, data mesh-oriented analytics platform. I helped the client establish the new platform data modeling standards, ETL/ELT technology, standardized ELT design patterns, data organization, and data flow. I conducted POCs with dbt (chosen as ELT), Snowflake stored procedures, and Prefect (for data orchestration). I designed many core data entities for consumption via end users and both ODS and data mart structures.

A system to dynamically generate data warehouse storage structures (star schemas) from metadata

We designed and constructed a user interface that allowed business analysts to define data structures and initiate data warehouse refreshes. The system triggered a process to generate SSAS tabular models that served as data analytics dynamically.

We chose to make editing data in user interfaces the event triggers, which would refresh the data in the data warehouse structures. This choice enabled app users to refresh (on-demand, in near real-time) responsive data warehouse data and associated analytic cubes (with support for partial cube refreshes).

I served as lead architect and lead of the engineering team to construct this system over a two-year period (incremental value-added delivery). I contributed Transact-SQL code and SSAS artifacts. I was the primary thought leader in the design and technical features, which enabled the responsiveness of the system. This avoided traditional batch data warehouse ETL refreshes (ETL was eliminated), shortened refresh cycles exponentially through the support of partial DW content refreshes, and served the near real-time refresh responsiveness so critical for business processes.

I led a project to process a large set of published technology research documents to gain insights into technology topics covered, topic distributions over time, and sentiment related to those topics. We utilized Python to extract relevant content from XML sources, stored it in an intermediary relational database format, and then processed the content using NLTK and Scikit-Learn.

We stored the results of the processing in a format suitable for analytics, reporting, and data visualization. A dashboard navigation experience was constructed.

I served as technical project lead, business analyst, and solution architect to design the system and construct the visualizations and analytics most useful to achieve business objectives.

I also established the production Python execution environment, refactored data scientist NLP code to make it suitable for more generic processing, and created production performance-tuned code. I designed and built the data analytics data storage and all the Power BI data visualizations, data navigation, and navigation experience for researchers and managers.

Insights were gained as to sentiment related to tech topics, and the business was enabled to explore changes in sentiment and topics over time.

A series of web applications to navigate and query data content on a public website for clients. Datasets were published to the back-end system and then "released" to be available on the public website and front-end client web applications.

I served as a lead and solution architect of the engineering teams to construct a data store and associated REST APIs to query a set of dimensional datasets. I was the advocate and key advisor for the user experience so that clients could easily navigate, query, visualize, and download data subsets of interest for large-scale published data deliverables.

I utilized my specialized knowledge of dimensional data to advise on clients' realistic and useful experiences in digesting data content. Custom, innovative visual mechanisms were utilized to navigate the data content. In particular, it was imperative to prevent users from receiving empty results in filtering data. Therefore, I promoted a microservices approach so the user interface reacted to "clicks" and adjusted front-end content and choices, so there were never empty results.

We implemented a swift SSAS back end and API performance to deliver those behaviors; this was one of my core contributions to the project.

An SQL Server database, Microsoft Excel, and Microsoft Power BI-based application that monitored the workflow of data through a structured set of editing and modeling steps.

Automated triggering actions were embedded in end-user applications to signal and record status within the overall data set management process. We then utilized Excel and Power BI-based reporting to allow managers of data scientists and research analysts to monitor the overall readiness of data and inspect for quality prior to allowing the publication to customers.

I deployed a third-party forecast modeling tool, integrated it into the existing portfolio of data management applications, and enhanced it to utilize a proprietary matrix algebra-based best-fit algorithm to allocate changes in data across multi-dimensional data sets.

The proprietary matrix algebra algorithm was core to taking high-level modeled forecast results and cascading the changes to multidimensional data simultaneously for all dimensionalities of the cube space.

This was previously a very time-intensive process, which was difficult to ensure quality. The best-fit approach allowed research analysts to focus on a few data facts they "knew" to be true and figure out how to adjust detailed dimensional data to intelligently honor known facts while adjusting all related detailed data in a statistically appropriate manner.

I led the technical team to implement the proprietary algorithm in Java and Groovy and create data integration APIs for live data flows into and out of data stores during the algorithm execution process.

My primary tasks were architecting the solution, leading the engineering team, implementing the key components, and determining how to test and troubleshoot a very complex process.

A dynamic and flexible application to automate the generation of all client data deliverable content using metadata and content templates. This was driven by an event-oriented workflow engine that supported long-running task sets.

I led the solution architecture and design of this system, along with the engineering team. I made code contributions on the database back-end side (Transact-SQL) and in Microsoft Office automation (Excel VBA and .NET).

Reporting was created—first in Microsoft Excel and later using Power BI—to monitor system and deliverables status within the overall workflow and to report error capture.

My thought leadership contribution was to emphasize the metadata-driven process and the idea that new deliverable content could be added and generated without modifying the codebase. This needed a creative use of metadata and templates to refine the deliverables' visual output content, which included a variety of tables, aggregations, pivot tables, and graphs.

Over time, I contributed ideas to extend the system for generating file-based content (Excel) to data set generation for piping data into client-facing website apps.

Research analysts were self-empowered to generate time-critical client deliverables.

I served as the development lead on a large-scale enterprise SFA implementation of Salesforce.com and an associated reporting system for sales leadership.

Salesforce.com was configured to fit the company's sales process to optimize efficiency and provide sales management visibility to the sales pipeline and deal-close success.

I served as a liaison to the sales leadership, which included the GVP of sales, and led an external consulting team of 20 engineers and PMs. I prioritized the features implemented and incrementally delivered the highest-value apps, reports, and analytics. I led the incremental rollout and led a team of 10 engineers who took delivery from a consulting firm and enhanced the application portfolio over three years.

Sales processes were drastically streamlined, highlighting best practices that had the highest revenue contribution and improved close rates. The system served to enhance the business process and sales processes.

I was responsible for contributing to a design that could be measured to improve application adoption. I also carefully measured usage and constructed reports and dashboards, allowing the sales management team to steer actions to ensure adoption and successful outcomes.

I led the software engineering team to design and build Gartner's first enterprise data warehouse. This system integrated data from financial, order entry, sales, and CRM systems to provide operational views of company performance. A star schema was utilized as well as a snowflake dimensional design.

I led data architecture and data modeling and selected vendor tools and reporting/dashboard solutions. Informatica was selected as the ETL tool, and Oracle was for database and reporting. I served as an architect and led requirements gathering and documentation and helped implement the system. In the initial years, Oracle's reporting was changed to Microsoft Power BI after leading a cross-functional evaluation team to select an enterprise standard.

Insights were gained in what factors most influenced retention; which led to shifting sales actions to those which most impacted renewals. This ultimately allowed Gartner to achieve double-digit revenue growth.

My contributions in optimizing the data flows reduced the data warehouse refresh time from over eight hours to four and set in place strategies to expand subjects added to the data warehouse.

I designed and built a Python algorithm to transform incoming D365 schema metadata which had unreadable column names consisting of a single string of non-word oriented characters (as a result of Azure Synapse Link, Fivetran connector ETL).

The Python algorithm computed likely words based on consultation of a trained word dictionary to transform column names to readable, sensible transformed names. I maintained a word dictionary and coded Snowflake Stored Procedure to orchestrate processing and population of metadata tables.

Devised, designed, and built a set of dbt Jinja macros to read metadata content and dynamically generate transformed, standards-compliant column names.

The process short-cut dbt development time by %30 or more, assured consistency in column naming and via ephemeral models whilst avoiding additional materialized instances of data.

This helped redirect developer time to quality testing and dbt tests for higher-quality deliverables

This project brings talent management (Greenhouse) and core Dynamics 365 HR data domains to the Snowflake data platform.

I led the data engineering design, performed all data modeling, and served as the primary liaison to business stakeholders to define, prioritize, plan, and design all reporting content (Power BI). I defined KPIs and data visualizations that aligned with business needs, which included executive leadership dashboards tracking critical KPIs

I designed and built Power BI semantic models and all Power BI dashboards and reports, focusing on recruiting velocity, job candidates' progress through the recruitment process, and business-critical tracking of headcount and terminations KPIs. I led the data engineering team and served as technical project manager

Reporting content supported the migration of data from Workday to Dynamics 365 HR functions. I conducted data validation processes with complex SQL to understand the outcomes of the systems migration process. Greenhouse talent management reporting was transitioned to Snowflake and Power BI-based reporting.