Supply chain and procurement professional with nearly 10 years of experience across purchasing, vendor operations, and enterprise procurement environments. I started automating work in 2016 with Excel VBA macros that cut order upload time from about an hour to ten minutes. At the time, I did not think of it as programming. I thought of it as fixing something that was too slow.

That pattern kept repeating.

In early 2025, a work laptop froze during a large Excel PowerQuery join. Not just slow — full GUI lockup, daily crashes, and no viable path forward inside the tool. That was the point where I moved seriously into Python, tested WSL against native Linux, started profiling the difference, and began working down the stack.

Excel/VBA → Python → Pandas → Polars → Parquet → ramdisk I/O → Linux → syscall profiling.

My focus is data engineering, pipeline performance, automation, and understanding what actually happens underneath high-level abstractions.

The production systems I build at work are confidential, but the engineering patterns are transferable:

• Multi-source ingestion from SQL, enterprise systems with no API, email attachments, browser automation, and cloud object storage.
• Data transformation pipelines with explicit stage contracts, checkpointing, cache invalidation, and restart-safe scheduling.
• Fuzzy matching, confidence scoring, hidden join-key discovery, and cross-system record linkage.
• Parquet over CSV for columnar access patterns.
• Ramdisk-backed intermediate I/O to avoid unnecessary disk writes during pipeline execution.
• Vectorized operations instead of row-wise loops.
• Stage-level timing, bottleneck isolation, and before/after performance measurement.
• Automated publishing to Excel workbooks, dashboards, and downstream consumers.

A representative production pipeline I built runs daily with no manual intervention, processes hundreds of thousands of records across multiple source systems, and reduced manual task resolution from roughly 30+ minutes to 2–3 minutes per item.

Extracts municipal procurement data from a City of Tempe PowerBI Gov dashboard with no public export path.

The project reverse-engineers the DSR binary protocol used by the dashboard, including bitmask row reconstruction, ValueDict index resolution, and millisecond timestamp conversion. It extracts 9,502 contract records across 928 vendors representing approximately $23B in estimated contract value, then cross-references vendors against USASpending.gov federal award data.

No API access was available. The DSR decode was the only practical programmatic path.

This project demonstrates a core engineering belief: missing infrastructure is a constraint, not a blocker.

I use LeetCode problems as a benchmarking sandbox. When a problem has a one-line solution, I want to know what that line actually costs.

Recent example: From 4 Syscalls to 8,517 — What Doubling a Column Actually Costs Across the Stack

For a simple Pandas column update, I benchmarked multiple Pandas strategies plus NumPy buffer mutation, ASM, C++, Rust, and Polars. I separated warm in-memory execution from cold-start syscall surface using randomized round-robin timing and strace.

Key findings:

• Direct assignment was the fastest standard Pandas API.
• *= 2 was slower than = col * 2 in this benchmark.
• .loc[], .apply(lambda), .iloc loops, and .iterrows() all added measurable overhead.
• .to_numpy(copy=False)[:] *= 2 bypassed most Pandas assignment machinery and was the fastest Pandas-backed path.
• Pandas’ cold-start syscall surface was dramatically larger than native ASM/C++/Rust binaries.

Profile: leetcode.com/u/tUhGYEF4fl

Languages: Python, SQL, Bash, C++ learning, Rust learning Data: Pandas, Polars, NumPy, PyArrow Pipeline: systemd, ramdisk I/O, CDC watermarking, Parquet, stage profiling Automation: Playwright, COM automation Infra: Linux, conda/miniforge, cloud object storage

• AWS Certified AI Practitioner — Dec 2025
• AWS Certified Data Engineer Associate — in progress
• Springboard Data Analytics Career Program — Jun 2026
• MITx MicroMasters, Supply Chain Management — 2018–2019
• Master of Science, International Business — Hult International Business School, 2018
• Bachelor of Applied Science, Supply Chain Management — Broward College, 2017

LinkedIn: patrickryankenneth