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Mark Modesto

@markmodesto

Senior data engineer specializing in Azure/AWS ETL/ELT pipelines, data lake/lakehouse, and AI-driven validation at enterprise scale.

United States

Message

What I'm looking for

I’m looking to build enterprise data lakes/lakehouses on Azure or AWS—owning ETL/ELT, governance, and CI/CD, with room to apply LLM-based anomaly detection while partnering with Data Science and Product in Agile, mission-critical environments.

I’m a Senior Data Engineer with 10+ years building enterprise-scale data platforms, ETL/ELT pipelines, and cloud-based analytics systems across healthcare, manufacturing, and operations. I focus on scalable lake and lakehouse architectures that support BI dashboards, AI/ML-driven insights, and automated data validation.

At MSI (Jul 2024–Present), I designed and deployed Azure-based ETL/ELT pipelines using Azure Databricks, Azure Data Factory, SQL Server, Snowflake, Python, and PySpark—enforcing enterprise data governance, security, and compliance. I implemented AI-assisted anomaly detection and LLM-driven validation workflows that reduced manual effort by 65%, built reusable PySpark + dbt frameworks for 50+ pipelines (35% less development time), and led migrations to modern Azure Data Lakehouse architecture for near real-time reporting.

Previously at Health Catalyst (Jan 2019–May 2024), I delivered AWS ETL/ELT pipelines (S3, Glue, Lambda, Redshift, Snowflake, dbt, PySpark) with HIPAA-aligned security, and created AI/LLM-driven NLP pipelines to improve downstream ML outcomes by 30%. Earlier at Motorola (Jan 2015–Nov 2018), I built manufacturing ETL pipelines and optimized SQL/PLSQL performance (up to 3x), while mentoring teams and leading Agile delivery to keep pipelines reliable, observable, and analytics-ready.

Experience

Work history, roles, and key accomplishments

Current

Senior Data Engineer

Current

MSI

Jul 2024 - Present (1 year 11 months)

Designed and deployed enterprise-scale Azure ETL/ELT pipelines across multi-terabyte ERP and operational datasets, enforcing governance, security, and compliance. Implemented LLM-driven anomaly detection and dbt/PySpark standardization, cutting manual validation effort by 65% and reducing development time by 35%.

Azure Databricks Azure Data Factory Snowflake Pyspark DBT Python Azure SQL Power BI Data Governance

Senior Data Engineer

Health Catalyst

Jan 2019 - May 2024 (5 years 4 months)

Built AWS ETL/ELT pipelines for clinical, claims, and operational healthcare datasets with HIPAA compliance, improving automated monitoring and trusted delivery of analytics datasets. Developed AI/LLM-driven NLP workflows and automated data quality validation, reducing manual intervention by 60% and improving downstream ML model accuracy by 30%.

AWS S3 AWS Glue AWS Lambda Amazon Redshift Snowflake Pyspark DBT HIPAA compliance Cloudwatch

Data Engineer

Motorola

Jan 2015 - Nov 2018 (3 years 10 months)

Developed and maintained large-scale SQL Server/Oracle and PySpark ETL pipelines for manufacturing and operational telemetry, enabling dashboards for predictive maintenance and fault detection. Automated transformation and validation workflows, reducing manual effort by 70%, and optimized SQL/PL-SQL queries for up to 3x faster reporting performance.

SQL Server Oracle Python Pyspark Pl SQL Performance Optimization Data Modeling