Paul Schmidt

• Upgraded daily batch pipelines to real-time streaming with Kafka on Confluent, reducing data latency to ~5 minutes to enable low-latency operational reporting from the analytical database. • Unified HR information systems (salary and hourly payroll, benefits, review processes, training) into automated pipeline to automate integrations while improving data quality. • Architected dual-database analytics system to separate low-latency queries from compute-intensive batch workloads. • Migrated core transactional DB from Heroku Postgres to CrunchyData. • Supported 40+ analysts for a 2,000-employee business as part of a 2-person data engineering team. Tech Stack: Python, Snowflake (Snowpark, Streams, Tasks, Dynamic Tables, Streamlit), AWS (Lambda, DynamoDB, ECS/ECR, IAM, SNS), Airflow, Docker, LocalStack, dbt, Looker

• Lead analytics engineer for Finance, Operations, Retail, Culinary, Supply Chain, and Customer Support. • Redesigned Retail Forecasting system for 3,000+ stores ($43M in annual sales) by incorporating features like product shelf life, shelf fullness, expiration patterns, regional demand, and protein preferences, reducing food waste by 30%. • Partner with stakeholders to build dbt models in Snowflake to self-serve department analytics. Tools: Snowflake, dbt, SQL, AirFlow, Git, Jupyter, Google Sheets, Slite

• Lead forecaster for national meal delivery business with 200,000+ weekly orders and 620,000 meals. • Analyzed trends in customer skips, pauses, and acquisition trends to inform planning and procurement decisions with Supply Chain, Culinary, and Finance. • Redesigned the forecast engine to limit extreme shifts, reducing volatility by ~26% across top-volume meals and proteins. Tools: Google Sheets, Google Apps Script, SQL, Excel

Building a personal timeline app that helps people reflect on their lives through interactive timelines and private journaling. After moving frequently around the world growing up, I became fascinated by how we preserve and make meaning from our memories. Mily helps people rediscover patterns in their experiences and share their life stories with the people who matter most. Currently working with early users to understand how people want to capture and reflect on their most meaningful moments.

• Own and manage a portfolio of several residential and commercial properties. • Oversaw end-to-end renovations (prioritizing scope of work, contractor selection, permits, budgeting) while working full-time in tech.

• Leased and managed apartments for 10 large-scale residential properties across Chicago. • Oversaw day-to-day operations for 800+ rental units as liaison between residents, maintenance, and property management teams. • Conducted competitor pricing analysis and optimized dynamic rent pricing weekly using LRO (Lease Rent Options). • Gained foundational experience in real estate, pricing strategy, customer service, and high-volume sales environments.

Mentor-guided, intensive data science and machine learning program with Python for data analysis and visualization, supervised learning (regression, ensemble, and similarity models), unsupervised learning (clustering, neural networks, and NLP).