Effective Team Leadership: Oversaw eight direct reports on a geographically distributed team of data analysts and data engineers. Led expansion of the team from three to eight, with strategic hires to enhance the team's capabilities and support long-term data initiatives. Substantially improved cross training, collaboration, and mentorship among team members Operational Stabilization and Efficiency Enhancement: Spearheaded efforts to stabilize existing data operations by strategically addressing scheduled job performance. Achieved a remarkable 70% increase in the speed of overnight jobs, optimizing overall system efficiency and data processing timelines. Successfully reduced the job failure rate from an average of three failures per week to just three per month, mitigating operational disruptions and ensuring data continuity. Cloud Data Warehouse Implementation: Initiated and drove a groundbreaking project to establish the enterprise data warehouse using Snowflake cloud data warehouse on the Azure platform: Pioneered company's transition from traditional on-premises databases to its first cloud-based database, enabling improved scalability, performance, and data accessibility. Regulatory Reporting Excellence: Successfully orchestrated the creation of 30+ critical regulatory reports related to Medi-Cal and Medicare as mandated by state and federal authorities within a six-month timeframe. Modular ELT/ETL Architecture: Architected a ELT system that ensured flexibility, enabling the company to seamlessly transition between ETL vendors with minimal rework. Led Agile Transformation: Successfully transitioned the team to adopt Agile project management methodologies, enhancing collaboration, responsiveness, and delivering projects with increased efficiency.

The CEO DailyIf you want to use data to drive outcomes, there is no better ally than the CEO. A passing comment by the Chief Executive Officer led to the creation and constant iteration of a Tableau dashboard that not only brought alignment to the C-Suite, but allowed C-level leaders tounderstand where to focus their energy. It also created a platform where the Data and Analytics team could escalate important findings to key decision makers. If you want data to have an outsized impact in an organization, being able to place it where it can be seen is vital.Selection and Implementation of the “Future-Proof Data Stack”Tableau + Snowflake+ dbt + FivetranThe rapid growth leading to a $1 billion dollar valuation and the expansion following that valuation stresses every aspect of the Data and Analytics infrastructure. Upon taking ownership of the Data and Analytics team it was clear the legacy stack would not meet CloudBees needs. After evaluating a number of products, we identified Tableau, Snowflake, dbt, and Fivetran as the stack that would meet the requirements of the entire corporation, now and into the future. By combining “quick-wins” with Tableau along with larger transformational changes, the team began delivering value immediately while still completing the transition to the new stack ahead of schedule.Implement Continuous Data Governance / Data Quality paradigmVirtually every data professional has been involved in a large data cleanup project, only to see the data becoming dirty again within days of project completion. The Continuous Data Governance Project alleviated this problem by using Tableau to monitor and track data quality issues over time and alert individuals who created issues when a problem was detected. Key factors in the success of this project: Educating the creator of a data issue on the business impact of the problem and tracking of data quality metrics over time, allowing stakeholders to see incremental and continuous progress.

Sometimes, a “greenfield data opportunity” actually means “we’ve neglected doing any instrumentation for years and we are in need of leadership”. Here’s how the project actually shook out: add telemetry to twelve existing applications, some internal, some external, some deployed and managed by customers, some SaaS, with a team composed only of engineers who are moonlighting on your project while taking a break from their “real work”. The result one year later? All twelve applications sending data back to both a Postgres database and a behavior analytics platform (Scuba/Interana) via Segment. And, most importantly: analytics based on that data being used by Product Management, Product Engineering, and Product Support to establish priorities and confirm their understanding of our customers. How was this done? By breaking most of the traditional Data and Analytics rules and drafting all of the Product Organization to help meet our goal.

MySQL, AWS, RDS, Tableau, Data Architect, scrum master. And that was just the first week...

“Data! Data! Data!" he cried impatiently. "I can't make bricks without clay.” - Sir Arthur Conan DoyleAt Lytx, there's plenty of "clay" but the challenge is turning it into "bricks". The data is flying in 24/7 from +200,000 devices deployed around the globe. As a senior member of the Business Intelligence Team, I had hands on experience architecting “version 0” of the data warehouse (DW) and then collaborating with a larger team to design and build the enterprise Data Warehouse on the GreenPlum/Postgres Massively Parallel Processing database platform (MPP). As the project matured, our team established company best practices and data quality/data governance standards to streamline and accelerate development. When excessive load on the transactional OLTP system led to multiple “Code Red” outages, I worked to tune specific areas of the SQL Server database to improve overall performance. While serving as both Scrum Master (Scaled Agile Framework) and SSIS developer, our team designed and built ETL/ELT using SQL Server Integration Services. With the Kimball design methodology, we were able to quickly begin providing value from the DW, visualizing data on corporate dashboards using both Tableau and SQL Server Reporting Services (SSRS).

It's about helping people make smarter decisions. Decisions based on information. To do this I lead a team of four Business Intelligence professionals. We build dashboards and provide data sources which let people rapidly aggregate large amounts of data, identify trends, and spot outliers.(If you want to get geeky about it: ETL, Data Warehouse Architecture, OLAP cubes, pivot tables, dashboards)Directed four person business intelligence team from reactive production support to creating strategic tools which are a vital to 400 person firm’s decision making process.Primary components of this effort were:• Release of new generation of executive dashboards supporting firm’s sales efforts.• Improving completeness of data warehouse data in core subject areas.• Identifying ad hoc reporting tool for use by advanced users and architecting framework to support users with minimal intervention from business intelligence group.Minimized production support issues and accelerated team’s development time by establishing best practices (both company specific and industry wide).Led upgrade of Data Warehouse from SQL Server 2005 to SQL Server 2008R2.Establish light weight code review process which resulted in improved collaboration and cross-training.As the team lead, I implemented a kanban style agile development methodology in order to keep group focused on primary objectives.

Who's stealing the drugs? CareFusion's appliances are used throughout hospitals to track who is administering which controlled substances. If you take the information from all those appliances across a hospital network and aggregate it together you start to become very curious about the outliers... (Data Warehouse Design,ETL Development,Database Development Best Practices,Database Build and Deploy Processes)

While supervising a team of three DBAs/Database Develoers, created high level of user confidence in reports generated by Reporting and Analytics team. This was achieved through a combination of technical changes to improve data quality as well as regular sessions with power users to address their concerns. The Director of Operations remarked: When his team sees strange numbers on a report they now suspect a problem with their process as opposed to a problem with the report. Moved business intelligence program from theory to production with limited time and resources. This process involved designing and implementing a star schema data warehouse, ETL processes (SSIS), OLAP cubes (Analysis Services), and reports (Reporting Services).Eliminated widespread database performance issues which were inhibiting throughput rates and hampering overall user experience. By implementing a combination of broad based and focused fixes performance was improved to the point that when transaction volumes doubled there were no reports of performance issues from users.Implemented standard set of database design guidelines across applications. This resulted in improved maintainability, increased data integrity, and better database performance.Replaced error prone manual build and deploy routine with streamlined process using Visual Studio Team Suite for Database Professionals. This allowed the team to build more frequently and catch potential issues earlier in the development cycle. Database deployments to production went from being a stress point to a routine event (even with multiple versions of the database in production simultaneously).

Converted existing normalized database into a dimensional model to better meet requirements, improve system performance, and speed future development.Designed SSIS framework for ETL processing. Use of framework accelerated ETL development and resulted in simplified maintenance due to standardized nature of packages. Implemented version control, and automated build and deploy process for database objects. Build and deploy time dropped from 40 hours per month to 8 hours per month.Created ETL packages to load both dimensional and normalized databases using SSIS.Refactored transactional database to consolidate DB code, enhance system performance and improve maintainability. Changes led to an 80% reduction in the number of tables in the database and the elimination of over 100 stored procedures. Co-authored company database guidelines and best practices.

Supervised team of eleven database professionals for investment advisory firm managing over $100 billion in assets. Team’s responsibilities included: Database administration (SQL Server and Informix)Data modelingSQL development (stored procedures and ad hoc requests)Performance analysis and tuningReporting Services developmentData warehouse developmentOLAP Cube / Analysis Services Development (SSAS)Production SupportHighlights during this period included:Implementation of multi-currency back office system which replaced 80% of firm’s legacy applications and databases over a two year period.Crafted and developed SSIS based ETL process to populate data warehouse fact tables and slowly changing dimensions. Implementation of proactive stored procedure tuning program which resulted in substantial improvement in overall database performance.Collaborated with team to define ideal data warehouse design based on business needs and implemented data warehouse to support OLAP analysis.Technical Lead for “Historical Data Load” project. Team successfully transitioned 25 years worth of USD based transactional data from legacy Informix database to new multi-currency based SQL Server schema using a combination of Unix scripts, Stored Procedures, and SSIS packages.