# David J.A. Charles Codrington III > Senior Data Engineer | Backend Engineer | PySpark | Databricks | AWS | ML | AI | LLM Location: New York, New York, United States Profile: https://flows.cv/davidjacharlescodringtoniii Highly-motivated and experienced engineer with background in fintech data engineering, data science and defense/government data. Experienced in developing scalable big data pipelines using cloud based tools that ensure data quality and optimize data engineering pipelines. I've developed my proficiencies in these areas: Programming Languages: ‣ Advanced in Python, SQL, PySpark and programming paradigms such as object oriented programming and functional programming Backend Development: ‣ Experienced with modern backend frameworks including FastAPI and Flask for Python APIs, and NestJS with TypeScript for scalable, production-grade backend services ‣ Proficient in TypeScript for backend development with strong knowledge of modern design patterns including Clean Architecture, Domain-Driven Design (DDD), and SOLID principles ‣ Build and manage microservices architectures using NestJS with gRPC and REST, enabling modular, high-performance internal and external service communication ‣ Implement robust authentication and authorization flows using JWT, OAuth2, and role-based access control (RBAC) ‣ Write well-documented APIs using OpenAPI/Swagger, with proper versioning, DTO separation, error handling, and response standardization Data Engineering: ‣ Proficient in building scalable data pipelines using Databricks, Apache Spark (PySpark), and SQL, handling batch and streaming workloads efficiently across large-scale datasets ‣ Designed and orchestrated robust pipelines using Apache Airflow, Prefect, and DBT, following modular and testable architecture for data transformations and analytics engineering ‣ Advanced in MySQL, PostgreSQL, AWS RDS, Snowflake, and Google BigQuery, with expertise in OLTP/OLAP modeling and building data marts and star schemas ‣ Built and maintained data lakes using AWS S3, GCP Cloud Storage, AWS Glue, Athena, and Boto3/AWS Wrangler for metadata and catalog management Quantitative Development: ‣ Strong understanding of financial markets and trading systems, with experience working on forecasting models and algorithmic trading strategies ‣ Developed and tested quantitative models using Python and C++ (in progress), with a focus on signal generation, statistical arbitrage, and support/resistance strategies ‣ Proficient in implementing momentum-based and mean-reversion trading strategies, including backtesting frameworks with millisecond-level accuracy ‣ Familiar with performance-critical programming and optimization techniques for low-latency strategy development ## Work Experience ### Senior Data Engineer - AI/ML @ Publicis Groupe Jan 2025 – Present | New York, NY ### Data Engineer @ Quashie AI and Analytics Lab Jan 2019 – Present ### Product Manager @ Quashie AI and Analytics Lab Jan 2024 – Present | New York, New York, United States • Led the end-to-end product delivery of two fintech MVPs, defining requirements, success metrics, and cross-functional execution across mobile, backend, and compliance teams. • Owned the product strategy and roadmap for wallet creation, stablecoin transactions (mint/burn/bridge), and admin governance flows, prioritizing features based on user value, risk, and regulatory constraints. • Translated complex blockchain, custodial, and banking integrations into clear product specifications and collaborated with engineering to deliver secure REST and gRPC APIs for high-stakes financial operations. • Drove cross-chain product capabilities by aligning engineering, security, and compliance teams to support EVM and Solana networks, including multi-signature approvals and raw transaction signing. • Defined user journeys for guests, registered users, admins, and institutional clients, ensuring seamless onboarding, MFA-protected authentication, and scalable role-based access control. • Launched event-driven orchestration as a product capability, enabling predictable transaction lifecycles and real-time observability using AWS EventBridge, Step Functions, and audit-grade logging. • Established API versioning standards, error models, and documentation quality bars, improving DX for mobile and platform teams and reducing integration time by creating a unified API contract. • Managed stakeholder alignment with compliance (OFAC/AML), risk, DFNS, and core-banking partners, ensuring product decisions met regulatory requirements while still achieving time-to-market goals. • Built data schemas and dashboards to track product KPIs, including transaction success rates, signature quorum attainment, blacklisted wallet enforcement, and cross-chain throughput. • Acted as the technical liaison between engineering, compliance, and leadership, simplifying blockchain concepts, identifying product risks, and influencing architectural decisions for scalability. ### Backend Software Engineer @ Quashie AI and Analytics Lab Jan 2019 – Present • Led the end-to-end product delivery of two fintech MVPs, defining requirements, success metrics, and cross-functional execution across mobile, backend, and compliance teams. • Owned the product strategy and roadmap for wallet creation, stablecoin transactions (mint/burn/bridge), and admin governance flows, prioritizing features based on user value, risk, and regulatory constraints. • Translated complex blockchain, custodial, and banking integrations into clear product specifications and collaborated with engineering to deliver secure REST and gRPC APIs for high-stakes financial operations. • Drove cross-chain product capabilities by aligning engineering, security, and compliance teams to support EVM and Solana networks, including multi-signature approvals and raw transaction signing. • Defined user journeys for guests, registered users, admins, and institutional clients, ensuring seamless onboarding, MFA-protected authentication, and scalable role-based access control. • Launched event-driven orchestration as a product capability, enabling predictable transaction lifecycles and real-time observability using AWS EventBridge, Step Functions, and audit-grade logging. • Established API versioning standards, error models, and documentation quality bars, improving DX for mobile and platform teams and reducing integration time by creating a unified API contract. • Managed stakeholder alignment with compliance (OFAC/AML), risk, DFNS, and core-banking partners, ensuring product decisions met regulatory requirements while still achieving time-to-market goals. • Built data schemas and dashboards to track product KPIs, including transaction success rates, signature quorum attainment, blacklisted wallet enforcement, and cross-chain throughput. • Acted as the technical liaison between engineering, compliance, and leadership, simplifying blockchain concepts, identifying product risks, and influencing architectural decisions for scalability. ### Open Source Contributor @ LangChain Jan 2024 – Present Contributor to Langchain open source repository. Helped resolve dev container startup error for developers to continue contributing to Langchain open source repo. ### Data/Backend Engineer @ American Express Jan 2022 – Jan 2024 ● Designed and built batch ETL pipelines for fraud and risk management, using database tools like PySpark, BigQuery and Airflow for orchestration in loading data into Hive tables. ● Utilized KPIs and metrics to track the performance of new products and collaborated with product and fraud teams to create dashboards to track performance, set up alerts to flag outlier behavior. ● Developed and deployed risk triggers for fraud detection in banking products (e.g., checking, savings, commercial accounts). Designed automated workflows to identify high-risk activities and flag accounts for review by fraud risk managers. ● Analysis and Design: Conducted in-depth data analysis to define criteria for effective risk triggers, such as monitoring payee addition declines and Early Warning Systems (EWS) bank addition decline rates within 24 hours. ● Implementation and Automation: Set up daily automated processes to execute risk triggers, ensuring consistent and timely referrals to fraud risk managers. ● Successfully implemented multiple risk triggers that resulted in $1.5MM in annualized fraud savings. ### Data Engineer @ Overtone Jan 2024 – Jan 2024 ● Data engineer for the startup that scores news articles by the amount of factualness of each article, and uses this to predict article popularity and reshares. ● Developed a search feature for Overtone articles which implemented full text search across articles, using Algolia for document search and SQLAlchemy for retrieval, and using Google Cloud Runs to re-index the data, and regularly migrate data from BigQuery to Firestore ● Developed full stack search app for Overtone.ai, an AI based startup that evaluates factualness across news articles (Algolia, Python, and GCP, JS) ### Data Engineer @ ACI Worldwide Jan 2022 – Jan 2022 ● Data engineer for the digital payments platform, automated fraud analysis, and monitored client portfolios. ● Wrote Python scripts that calculated moving averages of various metrics to detect outlier behavior on a per client basis and automated analysis with daily cron jobs, outputting suspicious account behavior in csv file. ● Developed a script to normalize and monitor addresses for fraud prevention for brands, such as GAP, enabling detection and blocking as transactions from addresses with fraud rates exceeding defined thresholds. ## Education ### Master of Science - MS in Data Science Eastern University ### Bachelor of Science - BS in Applied Mathematics City Tech, CUNY ### Regents Diploma KIPP NYC College Prep ## Contact & Social - LinkedIn: https://linkedin.com/in/david-j-a-charles - Website: https://www.davidjacharles.com --- Source: https://flows.cv/davidjacharlescodringtoniii JSON Resume: https://flows.cv/davidjacharlescodringtoniii/resume.json Last updated: 2026-03-23