- Designed and deployed containerized AWS Kinesis python consumer and producer clients on Kubernetes (EKS), achieving high-throughput, real time ETL into AWS RDS databases or AWS S3.- Developed and automated a data lake on S3 utilizing medallion architecture, enabling ad-hoc analysis by teams with AWS Athena.- Designed and deployed data pipelines on AWS EKS, Lambda, and Glue (PySpark) to facilitate data transfer and transformation between AWS DynamoDB, RDS, S3 and Redshift.- Engineered replication software to stream change-data-capture from AWS RDS Aurora to S3, Redshift, and other relational databases, achieving scale through responsive re-sharding and horizontal container scaling.- Wrote terraform modules to provision AWS infrastructure and automate data pipeline generation.- Developed and maintained CI/CD pipelines to automate code testing, performance, and quality analysis across test, beta, and production environments.- Designed transactional and dimensional data models to optimize ingest and query performance.- Generated near-real time data dashboards in AWS Quicksight and Splunk to provide metrics on business operations and performance across Rocket Logic Platform.- Implemented RabbitMQ on Kubernetes to facilitate the communication and workload orchestration between containers.- Supplied data and collaborated with AI/ML teams to facilitate the training and development of Claude and GPT4 models.

- Developed cloud data pipelines using AWS ECS and Lambda in Python and Golang, and Glue to support accounting and analysis for institutional investments within the Solana, Helium, Horizon, and Graph blockchains.- Eliminated manual data tasks by accountants' and analysts, improving workflow efficiency.- Wrote SQL queries to generate views for insights, and automated databases with stored procedures and triggers.- Analyzed AWS Aurora database instance resource utilization metrics to ensure and monitor query and ingest.- Built three APIs in Golang that standardize and simplify interactions and transactions across blockchain protocols.- Developed Python libraries around monitoring using Datadog and standardized ETL using Pandas, PyArrow, and PySpark.- Created a standard CI/CD process for building, linting, and measuring code coverage for data pipelines.- Provided mentorship and guidance to a team of eight engineers, overseeing codebase and cloud development and conducted routine quality and performance audits.- Engineered real-time ETL pipelines using AWS Kinesis & Lambda to load and transform large volumes of data in the data lake.- Wrote Spark jobs to transform and partition data objects in S3 for efficient Snowflake ingestion and automated data catalog population.- Collaborated with finance, product, and software teams to identify data need areas and process requirements to inform pipeline design.- Designed flow charts to visualize and explain data flow and ETL across AWS cloud resources.- Wrote Terraform to standardize and automate the deployment of AWS cloud resources.

- Maintained a perfect freelancer rating (5/5) and a 100% job success score and obtained Upwork’s Top-Rated Award (top 10% among all freelancers).- Completed 27 data analytics, data engineering, and BI projects.- Performed statistical analysis including multivariate regression, time series forecasting, cluster analysis, and mean comparison (metric and non-parametric).- Compiled technical reports that summarized the interpretation of statistical tests, data analysis, and development of research methodology for critical insights and actionable data-driven feedback.- Developed data pipelines in Python using Spark and Pandas automate data ETL between external APIs and relational databases.- Created digestible Tableau dashboards and visualizations involving geospatial, qualitative, and quantitative data to drive continuous monitoring and analytics.

- Managed large-scale datasets and ETL processes sourced from state and local government justice agencies using Python and IBM SPSS for the MFJ data portal.- Successfully onboarded data from 5 states into MFJ's data portal, meeting the ambitious 20 states by 2020 target and setting a record for the fastest completion rate among data managers.- Performed data modeling influenced by research methodologies to drive data analysis and optimize data processing.- Leveraged descriptive, exploratory, inferential, and predictive data analysis to develop data-driven performance metrics for policing and corrections agencies.- Developed data dashboards and visualizations in Tableau to effectively present geospatial arrest, procedural, and calls-for-service data metrics to government stakeholders and facilitate data-driven decisions.- Led and mentored a team of 3 research associates and interns, fostering skill development and promoting best practices in data management.

- Assisted with the oversight and coordination in evaluating a government-funded project involving police body-worn cameras and their connection to service results for the Rochester Police Department.- Conducted 100 hours of interviews with police patrol and supervisory officers to collect qualitative data on perceptions of body-worn camera impact and effectiveness.- Wrote ETL scripts to transform, clean, and aggregate data to utilize in statistical tests.- Conducted time-series analysis forecasting that established statistical significance in reducing civilian complaints and police arrest rates, and non-significance in reducing civilian calls for service.- Wrote and published a 280-page technical report on the evaluation of body-worn cameras on the City of Rochester’s website as well as recommendations for improving their usage to serve civilians better.