INDEPENDENT ENGINEERING & MSc RESEARCH

Project case studies

Read how I partitioned REES46, tested forecasting models on OTTO, and worked through a release failure in FulfillAI.

REES46 / DATA ARCHITECTURE01

Bronze411,709,736 historical events

RAW → PARQUET

Silver410,325,314 canonical events

VALIDATE / DEDUP

Monthly GoldPartitioned, restartable outputs

FEATURES / FACTS

Then: retrieval → ranking → local API

01 / REES46 V2DATA ENGINEERING · RECOMMENDATIONS

Making a 411.7M-event pipeline restartable

My first aggregation ran out of temporary storage. I split the work by month and session bucket so I could reuse finished work after a retry.

Python
DuckDB
Parquet
FastAPI

411.7Mhistorical events processed

Read the case study Source code

FULFILLAI / DELIVERY ARCHITECTURE02

{ }SourceGitHub

→

✓Build & scanActions · WIF

→

◇ImageExact digest

GIT-BACKED DESIRED STATE↓

GKE AutopilotArgo CD reconciles the Helm workload

API LAB

Health → metrics → alerts → recovery

02 / FulfillAIDATA & ML · CLOUD PLATFORM LAB

Rehearsing a bad release on GKE

I built a pipeline on synthetic fulfillment data, then set up a separate GKE API lab. I released a deployment pointing to a nonexistent image, then verified recovery.

PostgreSQL / dbt
GCP / GKE
Terraform
Argo CD

50,000synthetic orders in the data workflow

Read the case study Source code

OTTO / SELECTED CV RESULTS03

Selected on CV, before the final holdout

03 / OTTO forecastingMSc RESEARCH · TIME-SERIES FORECASTING

Why a weekly baseline won on OTTO

I compared eight forecasting approaches on four weeks of hourly cart and order counts. Repeating last week’s pattern gave the lowest mean WAPE on the development folds.

Python
PyTorch
scikit-learn
Time-series CV

8approaches compared chronologically

Read the case study Source code

OPEN TO JUNIOR OPPORTUNITIES

Get in touch

I’m looking for junior Data and ML Engineering roles. If you have a role in mind or a question about the work, I’d like to hear from you.

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