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Martin Chavez

@martinchavez4

I build scalable, compliant data platforms powering analytics, ML, and real-time product decisions.

At Fictive Kin, I own end-to-end data platforms for SaaS and healthcare products, ingesting billions of daily events into Snowflake and Databricks with Kafka, Spark Structured Streaming, and Airflow. I standardized 300+ dbt models, cut analytics development time by 40%, and reduced cloud data spend by 35%.

I've built healthcare, SaaS, and fintech pipelines at Gradient Health, Atlassian, and Collectly, from HIPAA-compliant patient-data workflows to product analytics and fraud-detection features. I focus on reliable batch and streaming systems, data quality, governance, and low-latency analytics and ML data products.

Experience

Work history, roles, and key accomplishments

Current

Senior Data Engineer

Current

Fictive Kin

Nov 2023 - Present (2 years 10 months)

Architected and owned the end-to-end data platform for SaaS and healthcare products, ingesting billions of events per day into a Snowflake + Databricks lakehouse using Kafka, Spark Structured Streaming, and Airflow. Built a modular ELT framework with dbt and Python, standardizing 300+ models across bronze/silver/gold layers and cutting analytics development time by 40%.

Snowflake Databricks Kafka Spark Airflow DBT Python Terraform GitHub Actions Great Expectations OpenLineage HIPAA IAM

Data Engineer

Gradient Health

Feb 2020 - Sep 2023 (3 years 7 months)

Built large-scale ingestion and transformation pipelines with Spark, Pandas, and Airflow, preprocessing 10M+ patient records and DICOM medical images for analytics and ML training. Designed the healthcare data warehouse on Snowflake and Redshift with conformed dimensions and slowly-changing dimensions, powering provider directory and imaging analytics.

Spark Pandas Airflow Snowflake Redshift Kafka AWS Kinesis S3 Parquet Delta Sagemaker Docker Kubernetes Prometheus Grafana ELK HIPAA

Data Engineer

Atlassian

Mar 2018 - Dec 2019 (1 year 9 months)

Built product-analytics and event pipelines for enterprise SaaS collaboration tools, aggregating user-behavior data with Spark, Pandas, and SQLAlchemy for reporting and ML modeling. Modeled and maintained the analytics warehouse on PostgreSQL and Redshift, reducing report query latency by 40% through indexing, materialized views, and partitioning.

Spark Pandas SQLAlchemy PostgreSQL Redshift Airflow Plotly D3.Js Tableau Jenkins GitHub Actions Docker

Data Engineer

Collectly

Jul 2016 - Feb 2018 (1 year 7 months)

Built ETL and data-preparation pipelines in Python (Pandas, NumPy, Spark), handling millions of fintech and healthcare-billing records for analytics and ML models. Designed data models and warehouses in PostgreSQL and MySQL supporting fraud detection, eligibility verification, and patient risk scoring.