Chicago, Illinois, United States
Jasmine began managing data in 2008 in the U.S Navy. She has over a decade of corporate data and service delivery experience within our Nation's largest industries. Jasmine specializes in Big Data Modeling, Analytics, Software Engineering, Digital Solutions and Operational Excellence. As a Data Engineer practitioner, she is experienced in ETL/ELT data pipelines, Data Lake/Warehouse management, Lambda Architecture, Scaling operations in Agile environments, Cyber Risk, Cybersecurity Networking and Cyber Intelligence. She's built frameworks in multi-cloud infrastructures including AWS, Azure and GCP. As a key leader in business units, she's typically engineering solutions "hands-on-keyboard" almost exclusively for mission-critical projects used in executive decision-making. Experience and Technical portfolio includes: Python (data engineering, analytics, machine learning, software development, data modeling, unit testing), SQL (advanced queries, exploratory analysis, distributed computing), Java (microservices, software development, unit/mock testing, cyber resilience), Javascript (front-end development, data visualization), HTML (front-end development, web design, microservices). Her ETL/ELT data pipeline experience includes: Apache Hadoop, Apache Spark, BigQuery, Amazon Web Services (S3), Google Cloud Platform (BigQuery, Data Studio), Microsoft Azure, Snowflake (ELT), Salesforce (API Software Engineering), and Github. Her enterprise production portfolio includes: Operations, Strategy, Cybersecurity, Cyber Risk, Risk Management, Disruption and Digital Transformation, Product Management Strategy, DevOps, Full-Stack Software Engineer Leading, Business Intelligence, Sales Engineering, Marketing Technology, Artificial Intelligence Research and Engineering, Process Mining (Celonis), Center of Excellence Ownership (Google Cloud), Data Privacy (Discovery for GCP Data Lake).
- Received perfect score on July 2022 Enterprise assessment - Co-leads Strategy and Disruptive transformation of Digital Software Engineering Management - Leads general big data and analytics critical data modeling portfolio
Creates, designs, architects and engineers consumer data products. Leads capabilities-thinking with solidarity towards business value-add. Operationalizes consumer data platforms to enable optimal production of consumer data platforms. Underwrites Cybersecurity and cyber risk processes to enhance product viability, while harnessing key outcomes throughout the end-to-end platform life cycle. Advocates for Data Engineering best practices where principled development demands precedent. Informs stakeholders of value-driven future opportunities where velocity and momentum are demonstrated requisites.
Works with and reports directly to the Head of Platform Engineering for Kraft Heinz's digital transformation journey, leveraging cloud technologies to catalyze enterprise OKRs as well as memorialize significant data engineering practices.
I assess, analyze and engineer solutions mitigating inherent and exigent risks related to auto finance sales data.
- Negotiated cluster platform design, securing customer at pre-sale stage, enabling 40% cost-savings - Created enterprise analytics reporting program, leveraging Python, SQL, Snowflake, AWS, Linux and Github - Managed customer service and security expectations, coordinating utilization and efficacy of technical resources Technical Specifications: AWS, Snowflake, Apache Spark, Apache Hadoop, Jenkins, Microsoft SQL, Tableau, Jira
- Contributes product improvements to Adobe Engineering team for release - Tester for Spark Post Video Beta (Sept 2019 - Present) - Designs promotional material for Executive team - Creates designs to be added to community
- Improved curriculum supervised learning model, increasing accuracy score by 2% - Championed development of niche collaborative environments in Slack to improve student communication - Relayed strategic insights on tools and infrastructure within modules to core product team Notable projects include: - Predicting Class/Income Level with Supervised Learning ● Attempts to predict the income of Census respondents using Logistic Regression and Recursive Ensemble Modeling. ● Tech: Gradient Boosting Regressor, Random Forest Classifier, Linear SVC, Logistic Regression, Pandas, Seaborn. - "Do They Like Me?" Google A/B Testing with Keyword Searches ● Attempts to test the significance of Google’s Search Term service to gauge relevancy of popularity. ● Tech: Matplotlib, Pandas, Seaborn, AB Test.
Modis Academy Scholar - Data Science with full-ride scholarship