The Data Plan is a forward-thinking startup dedicated to empowering businesses to harness the power of AI and their proprietary data to drive automation and innovation. We specialize in providing tailored data and AI engineering solutions that streamline operations, enhance decision-making, and unlock growth potential. Through a focus on education, cutting-edge technology, and strategic implementation, we help businesses seamlessly integrate AI into their everyday processes.

Responsible for Global Data Foundation Platform and organizational structure for data engineering, AI/machine learning, and DevOps/MLOps for a large retail organization that manages a portfolio of premium outdoor and active wear brands. Directed multiple international teams in constructing data products tailored to both individual brands and central business Analytics teams. Cultivated High-Performing Data Teams – Organized and deployed high-performing engineering teams by aligning individual strengths with organizational goals, boosting productivity by 30%. Fostering a supportive environment optimized team dynamic, yielding consistent exceptional results and a thriving workplace culture. Led data and machine learning projects, strategically assembling internal teams and supplementing with staff augmentation to avoid external vendor reliance. This approach fostered synergic collaboration, achieving substantial cost savings exceeding $1.5 million while upholding quality and innovation. Cross Functional Data Products – Integrated cross-functional team strategy, uniting data engineers, scientists, analysts, and product owners in close partnership with business stakeholders to develop impactful data products focused on strategic business area goals. Delivered measurable outcomes while focusing on business value, collaborative engagement with stakeholders, data quality and adoption. Advanced Technology Integration – Led the solution team responsible for building AI Foundation on AWS Cloud using Generative AI and Large Language Models for delivering new innovative products including new applications designed to produce photo realistic product images and generating compelling content for websites and marketing endeavors thus enabling innovation in product creation lifecycle. Data Platform Overhaul – Expanded and optimized data lake platform by 50% while redesigning company’s data architecture, streamlining data access and analytics for all stakeholders. Created a streaming data solution to ingest data from SAP systems, enhancing real-time decision-making. This development powered machine learning models and reporting dashboards, resulting in a 60% increase in essential data assets, all while lowering the overall cost of the platform. Machine Learning Engineering – Developed several predictive modeling solutions including Customer Segmentation, Sales Forecasting, Promotion and Price Optimization, Demand Forecasting and Customer Lifetime Value (CLV). Scaled the training, monitoring, and deployment of thousands of machine learning models in production by developing a feature store and robust MLOps pipelines, improving model quality and performance while reducing deployment time by 30%. Built an Observability Platform for Data Analytics - Increased system reliability, scalability and uptime while reducing costs by 25% by building an observability platform to monitor data pipelines, data quality, resource utilization, and analytical support across the global resources used for data analytics. Implemented Data Privacy and Compliance - Enhanced data privacy and compliance by working closely with Data Governance and Data Privacy teams to inventory and anonymize private data in accordance with CCPA regulations. This initiative not only ensured alignment with legal requirements but also bolstered customer trust and reduced the risk of legal penalties related to privacy violations.

Led multidisciplinary engineering teams in the strategic overhaul of data and machine learning pipelines, enhancing data warehousing and DevOps practices to meet the advanced analytics needs of a large-scale retail organization. Under my strategic guidance, we developed a robust set of Data Ops methodologies, improving our approach to data storage, integration, and deployment. This pivotal transformation led to a marked enhancement in operational efficiencies and streamlined workflows, catalyzing a culture of cross-functional collaboration and technological agility across the organization. Orchestrated the deployment of cloud-based solutions using AWS Cloud, driving predictive analytics capabilities that bolstered data-driven decision making, contributing to wider of use of enterprise data and uplift in business intelligence outcomes through process automation. Managed a comprehensive AWS Cloud Data Lake and Redshift data warehouse, optimizing data storage and retrieval processes to support enterprise-wide analytics initiatives. Streamlined development lifecycles by 30%, achieving greater operational efficiency and promoting a collaborative culture across technology and business units. Accelerated predictive analytics capabilities by 20% through strategic collaborations with data scientists and business leaders, directly impacting business strategies and performance. Strengthened data security through stringent governance and encryption techniques, aligning with regulatory standards and working closely with information security and corporate compliance teams. Managed a large portfolio of projects, performing ongoing prioritization and resource allocation to ensure that the critical business projects are delivered on time.

Responsible for Data Quality and Automation for the Enterprise Data Lake serving the needs of data scientists and manufacturing facilities to ensure quality of data from ingestion to making predictions. Manage several teams of data engineers, application developers and data scientists in Atlanta and Bangalore Implemented a data quality program for Enterprise Data Analytics team including IoT machinery sensor data and manufacturing asset data including AWS pipelines, Redshift data warehouse and SAS models. Built automation for Python models and data quality jobs utilizing serverless architecture with AWS Lambda, S3, Glue, Redshift and Aurora. Delivered a SAS project for Gypsum division, preparing and cleansing data, measuring quality of that data and deploying analytical ready datasets for Data Science teams utilizing SAS Viya and Event Streaming Manager. Developed an application with real time streaming data using Kinesis streams and model predictions to display machine faults and bottlenecks in real time for the plant operations on the floor. Developed a common DataOps strategy for management of 200+ streaming and batch data pipelines, 500 SAS models in production and data quality monitoring. Created a central monitoring and alerting solution used to report on job failures, quality metrics and model performance data for monitoring of data issues and model drift using a table-driven data model and communication. Worked closely with Data Governance team to develop data onboarding, data profiling and data cataloging processes for better data management and data sharing.

Managed several teams of data engineers, developers and administrators in the US and Poland supporting data engineering initiatives, database operations and large data migrations. Achieved $3M in cost savings through multiple projects to migrate 400+ database servers across data centers and to the cloud. Reduced labor costs 30% by building and training an offshore team in Poland. Developed and executed on cloud adoption strategy by evaluating cloud providers, architecting highly available infrastructure, and enabling data governance to mitigate compliance risks and cost. Played key role in architecting enterprise data lake by utilizing standard processes and tools for data collection, storage, and data visualization. Migrated Oracle data warehouse for the Beverage division to the cloud utilizing AWS serverless architecture including AWS Data Pipeline, Spark EMR, S3, Glue, Athena and Aurora services Developed an IoT solution for Connected Machines providing analytical dashboards to C-level suite to enable real-time monitoring and predictive models for Westrock’s Food and Beverage division. Implemented data compliance solution in response to GDPR regulation by using encryption, data masking and data governance processes. Delivered over $300K in cost savings by reducing commercial database footprint by migrating to open-source database platforms PostgreSQL and MySQL.

Managed two DBA teams accountable for administration and support of CDC data centers consisting of 290+ SQL Server and MySQL instances containing over 9,000 databases across 90 TBs of storage. Key member of the Cloud First initiative responsible for launching of government systems to AWS and Azure clouds.

Development and management of SQL Server systems, Data Warehouses and ETL pipelines supporting Centers for Disease Control and Preventions agency. Earlier experience dating back to 1999 included roles in Database Engineering, Web Development, and IT Project Management across diverse organizations and industries.