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Brian Schillaci

@brianschillaci

Senior Data Engineer at DailyPay who built real-time payroll processing and cut critical reporting jobs to under 30 minutes.

United States

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At DailyPay, I built a real-time payroll processing platform that ingested 50–100 million daily events from enterprise HR systems, calculating earned wage balances with near-real-time latency.

I also implemented an AWS S3 data lake consolidating customer transactional, payroll, and financial data, then optimized ETL pipelines to reduce critical reporting jobs from several hours to under 30 minutes.

Earlier at DailyPay, I worked on real-time fraud analytics, and at ICIMS I designed recruiting data pipelines and contributed to a resume intelligence platform. As a Software Engineering Intern at Sulzer US, I built Python applications and scripts to automate quality and reporting workflows.

Experience

Work history, roles, and key accomplishments

Current

Senior Data Engineer

Current

DailyPay

Oct 2023 - Present (3 years)

Built a real-time payroll data processing platform using Python and Spark Structured Streaming, ingesting 50-100 million daily events from HR systems like ADP and Workday. Led a data lake initiative on AWS S3 with Delta Lake and Airflow, reducing critical reporting jobs from hours to under 30 minutes.

Python Spark Kafka Amazon EMR S3 Delta Lake Airflow Snowflake Terraform Amazon EKS Aurora MySQL Prometheus Grafana GitHub Co Pilot

Data Engineer

DailyPay

Jun 2021 - Oct 2023 (2 years 4 months)

Worked on a real-time fraud analytics platform processing 20-50 million daily events through Kafka and Flink, enabling fraud detection within 500 milliseconds. Designed data infrastructure supporting 5,000+ transactions per second with exactly-once processing and immutable audit logs.

Kafka Apache Flink Amazon EKS Redis Elasticsearch Grafana PostgreSQL S3

Data Engineer

iCIMS

Jun 2019 - Jul 2021 (2 years 1 month)

Designed a scalable data platform unifying candidate and hiring data from multiple ATS customers using Kafka, AWS Glue, and Spark. Implemented data quality frameworks and schema evolution handling with Airflow and Great Expectations, reducing pipeline errors by roughly a quarter.

Kafka AWS Glue Spark Snowflake Airflow Great Expectations S3 Lambda Elasticsearch NLP

Software Engineering Intern

Sulzer US

May 2018 - Aug 2019 (1 year 3 months)

Built internal web applications using Python and Flask to automate quality inspection workflows, replacing manual spreadsheets. Developed REST API endpoints and database integrations with PostgreSQL to consolidate maintenance records.

Python Flask PostgreSQL REST APIs Jenkins Git CI CD