Oversaw a team of analysts who managed the data quality of 200+ data providers in North America, to enable real-time supply chain visibility for GM, Ford, Stellantis and other global manufacturers- Utilized Excel, PowerBI, SQL, Postgres, Databricks, Jira

- Managed a team of information analysts in an agile development environment, to develop and support data solutions for GM’s Customer Care and Aftersales department- Represented and supported the largest data hub in the department as a business product owner; managed user access, data quality, and acted as a liaison between IT and end users- Supported the modernization initiative to decommission and replace legacy warehouse mainframes- Eliminated redundant workflows and datasets by supporting the decommission and consolidation of a legacy data hub - Increased data accessibility by supporting the initiative to implement and onboard users on to a data federation tool called Dremio - Increased organizational efficiency and planning accuracy by developing and supporting enterprise resource planning tools- Reduced the head count required to run User Acceptance Testing (UAT) by developing and implementing a PySpark framework to automate data quality tests; the distributed computing solution enabled tests to run 30 times faster than a desktop application- Recognized by Global VP and the Operating Committee Executives for developing and implementing innovative solutions for the department- Utilized Excel, Access, Oracle, PowerBI, SQL, Python, PySpark, KNIME, Dremio, Hadoop, Hive, Jupyter Notebook

- Acted as a data owner by collecting, managing, cleansing and reporting out on the productivity data for 24 warehouses across North America- Determined data strategy and implemented data governance practices for the warehousing network- Collaborated with finance, plant managers, and executive leadership to develop and maintain centralized project roadmaps, KPI reports, and dashboards to provide insight on the performance of the warehousing network and the impact of cost saving initiatives- Enhanced oversight of employee performance by managing an application to track warehouse employee productivity- Utilized SQL, Excel, Access, VBA, Oracle, PowerBI

Project 1: Recommendation Engine For Dealer Vehicle Order Configurations- Developed a new data architecture and data pipeline to support a recommendation engine; the solution captures the daily inventory of national dealerships from a REST API and writes it to an Apache Kafka serverProject 2: Big Data & UI Solution For Retail Planning & Incentives- Led the development of a Hadoop based solution to process and analyze billions of records reflecting competitor incentive behavior, to optimize GM’s national and regional incentive spending- Developed a UI to enable business users to interact with and extract insights from curated datasets- Developed a self-service tool to automate reoccurring data processing activitiesProject 3: Mobile Data Reports For Sales Managers- Supported the development of mobile data reports for district sales managers- Led code reviews and coached team members on Python and PySparkProject 4: Data Migration Initiative For Cadillac’s Global Dealer Network Performance Team- Supported the initiative to migrate datasets from Cadillac’s source systems to GM’s global data warehouse- Utilized PowerBI, SQL, Hadoop, Hive, Python, PySpark, Jupyter Notebook, Shell Scripts, Apache Kafka

Project 1: Text Mining & Machine Learning Project With GM R&D- Improved classification accuracy by integrating 2 mathematical models into a supervised machine learning algorithm; SVD matrix and Naive Bayes- Developed visualizations to test and train the algorithm- Scaled the algorithm from a single threaded Java program to a custom MapReduce application for large scale data analysis on a Hadoop cluster; reduced processing time for large datasets by 80%- Developed an application to enable business users to utilize the algorithm on the Hadoop cluster remotely from Windows; eliminated the time and effort required to understand and use the Linux environmentProject 2: Business Intelligence Platform for Vehicle Safety Analytics- Collaborated with a multisite team to configure, stand up, and maintain a business intelligence platform- Acted as a business analyst by: collecting and reconciling data requirements from multifunctional end users- Acted as a developer by: connecting the data pipeline from Hadoop to RabbitMQ to Apache Storm to Elasticsearch- Acted as a data steward by: taking ownership of the data quality of the pipeline from end to endProject 3: Text Preprocessing Application For Vehicle Safety Analytics- Supported data science activities, by reverse engineering and integrating stand-alone algorithms developed by GM R&D, into the primary text preprocessing application used by the VSA team- Utilized Tableau, SQL, Teradata, Hadoop, Hive, Java, Python, PySpark, JSON, Shell Scripting, Batch Scripting Elasticsearch, and RabbitMQ

- Analyzed and optimized GM’s intercontinental logistics network utilization- Supported master data maintenance and the development of a weekly logistics operating plan for GM assembly plants in North America- Developed databases and automation tools to streamline business processes and analysis- Drove cost savings, eliminated waste and resolved network constraints via collaborative efforts with service contractors, plant managers, and suppliers- Total cost savings initiatives approved and implemented: $2.1 million- Collaborated with GM R&D on a cross-functional cost savings project; converted R&D’s SAS algorithm to VBA to enable business users to leverage the algorithm on Microsoft products- Total cost savings identified through cross-functional project: $6.4 million- Utilized Excel, Access, Tableau, SQL, VBA, SAS

- Led a project team in the development of a Microsoft Access database to integrate and streamline the forecasting, budgeting, and procurement components of the GM Knowledge Center- Led data modeling activities, determined data strategy and implemented data governance practices- Utilized Access, Excel, SQL, VBA