Transitioned from VP of Data Engineering to a Founding Engineer role, centering entirely on technical architecture and core execution. This deliberate pivot allowed me to build the high-impact data infrastructure required to scale the company to a $300M Series B valuation.

Core Architecture & Execution:

• Architected and built the core data linking engine, establishing a fundamental product value proposition for integrating, resolving, and mapping complex client datasets.

• Engineered the foundational data pipeline architecture from the ground up, providing the scalable infrastructure that currently powers all client-facing workflows and deployments.

Joined as one of the first engineering hires to bootstrap the data engineering function from pre-seed all the way to Series A. Led the initial architecture, built the foundational data pipelines from scratch, and helped establish the early engineering culture.

• Designed and deployed the V1 architecture utilizing Kedro, Dagster, MongoDB, Django, Cloud Run, and Terraform, establishing the core ingestion framework for the company.

• Delivered the initial client platforms that proved product-market fit, directly supporting the successful Series A fundraise.

• Led a cross-functional team of 7 to win a 2021 Microsoft Global Hackathon Challenge (out of 70,000+ participants), rapidly prototyping and delivering an award-winning technical solution.

• Architected and implemented a CMMC L3 compliance solution for the Federal Business Intelligence platform, engineering automated delivery and monitoring for over 130 critical security controls.

• Spearheaded the modernization of the Federal Reporting platform by fully transitioning legacy infrastructure to Infrastructure-as-Code (IaC) utilizing Azure Bicep.

• Engineered and deployed an optimized CI/CD pipeline that slashed system deployment times by over 50% while improving overall release reliability.

• Served as Lead Data Engineer for an internal analytics accelerator in the energy sector, architecting modular, reusable data pipelines using the Kedro framework to significantly reduce project delivery timelines.

• Engineered an automated data profiling and validation engine leveraging Great Expectations to enforce multi-dimensional data quality standards across diverse datasets.

• Architected an extensible framework for the profiling system that allowed for custom logic and automated remediation strategies when data quality violations were detected, ensuring high-fidelity inputs for downstream models.

Oil and Gas Client (Technical Leadership)

• Architected and led a team of 3 internal engineers and 3 client engineers to deliver a full-stack optimization platform for a major O&G client, generating $40M in annual revenue through real-time refinery sensor data analysis.

• Engineered a recommendation engine utilizing Azure Data Factory, Databricks (PySpark), and App Services to optimize plant operations and throughput.

• Acted as a strategic technical advisor, designing robust solution architectures and presenting deliverables to the client’s steering committee and executive leadership.

• Drove project sustainability by upskilling client engineering teams in Python, Git, and Azure cloud best practices, ensuring long-term architectural integrity after hand-off.

Mining Client (Predictive Engineering & Optimization)

• Designed and deployed a predictive maintenance pipeline on Google Cloud Platform (GCP) to forecast critical mining component failures with hourly precision, resulting in $2M in annual cost savings.

• Orchestrated complex data workflows using GCP Composer (Airflow), Cloud Functions, and Pandas to ensure high-availability model inference in a production environment.

• Developed an automated anomaly detection system utilizing ADTK to monitor sensor drift, establishing a robust feedback loop for model retraining and continuous performance stability.