Data Engineer

Diligent is a global provider of governance, risk, and compliance software solutions.

• Enhanced data accessibility by designing and building serverless data infrastructure and pipelines, ensuring reliable data flow and retrieval for key stakeholders.
• Overhauled data fetcher logic by transitioning data API fetchers from VBScript to Python, setting up database ingestion, and integrating scrapers into cloud infrastructure. Resolved issues related to data quality, performance, and rate limits using Python, AWS Lambda, CloudWatch, and MS SQL.
• Built, improved, and maintained data operations processes, analyzing and systematizing the issue resolution pipeline. Reduced issue resolution time and improved data and code pipeline reliability and maintainability using AWS CodePipeline, CloudWatch, EventBridge, SNS, Lambda, and Slack. Orchestrated multi-month backfills.
• Designed structured logging, notifications, and dashboards for comprehensive data and infrastructure monitoring.
• Reduced code build time by 75% through refactoring and expanded CI/CD capabilities to improve build reliability and developer experience using AWS CodeBuild, Lambda, CodeArtifact, ECR, Bash, and the GitHub API.

Aragon DAO builds governance infrastructure for decentralized organizations (DAOs).

• Developed reporting pipelines for DAO governance analytics, combining on-chain and off-chain data sources.
• Built a financial oversight dashboard using Python, Pandas, Dash, and Anvil, enabling real-time transparency for community stakeholders.
• Automated data retrieval from Discourse, Discord, Dework, and Dune, ensuring accurate and timely reporting for decentralized governance.
• Delivered high-quality data insights to support DAO decision-making, increasing transparency and accountability.

Worked independently on advanced analytics and engineering projects across multiple industries.

• Resolved data challenges across finance, Web3, DeFi, health, and energy sectors.
• Built an analytics pipeline for Terra arbitrage opportunities by collecting, processing, and analyzing on-chain Terra/Cosmos data using Flipside, Python, and pandas.
• Developed a time-series glucose forecasting model, achieving prediction accuracy comparable to market-leading commercial medical devices using Python, pandas, and scikit-learn.
• Built a reporting pipeline to assess a medical treatment device used in clinical trials, generating actionable insights to inform clinical decisions and device performance evaluations using Python, pandas, matplotlib, seaborn, and Jupyter.
• Engineered an evaluation pipeline for machine learning feature engineering methods using Python and scikit-learn.
• Wrote technical drill-down blog posts on Machine Learning, MLOps, SQL, and Python.

Dorsum is a software company delivering wealth management and investment platforms.

• Created documentation for a B2B wealth management SaaS, ensuring clarity and usability for both technical and non-technical stakeholders.
• Developed B2B business proposals highlighting platform capabilities and wrote white papers to support client engagement and content marketing initiatives.
• Contributed to regulatory analysis and compliance documentation, ensuring solutions aligned with banking sector requirements.
• Supported cross-functional teams with technical writing and marketing content, strengthening client engagement.