Open to opportunities

Ayesha Engineer

@ayeshaengineer

Message

I build governed cloud data platforms and real-time pipelines for healthcare and financial services.

United States

Message

What I'm looking for

I'm looking to build governed, scalable cloud data platforms and real-time data products where reliability, analytics, ML readiness, and regulated-data stewardship matter.

At Orion Health, I architect Snowflake and Databricks lakehouse platforms for payer-provider analytics, integrating 120+ clinical, claims, eligibility, FHIR, and HL7 sources. I've built PySpark, Kafka, and Kinesis pipelines that reduced ingestion latency to under 10 minutes and improved curated data availability to 99%+.

Previously at Banner Health and Sikich, I delivered AWS and Azure data platforms, migrated legacy workloads, and built reliable ETL/ELT systems for healthcare and financial-services clients. My work spans HIPAA-governed clinical data, PCI-aware payment data, CDC, data quality, semantic models, and analytics products.

I also lead code reviews, infrastructure automation, CI/CD quality gates, and observability practices that help engineering teams ship dependable data products.

Experience

Work history, roles, and key accomplishments

Current

Senior Data Engineer

Current

Orion Health

Jan 2022 - Present (4 years 7 months)

Architected Snowflake and Databricks medallion lakehouse for payer-provider analytics, integrating 120+ clinical, claims, eligibility, FHIR, and HL7 source tables. Engineered PySpark, Kafka, and Kinesis streaming pipelines processing multi-terabyte daily workloads, reducing ingestion latency from 40+ minutes to under 10 minutes.

Snowflake Databricks PySpark Kafka Kinesis Airflow Dbt Cloud Terraform GitHub Actions Datadog OpenTelemetry Unity Catalog HIPAA PHI RBAC Tokenization Encryption Data Lineage CI CD

Data Platform Engineer

Banner Health

Jun 2018 - Dec 2021 (3 years 6 months)

Designed AWS healthcare data platform using S3, Glue, EMR, Redshift, Airflow, and Snowflake, scaling monthly data volume from 18M to 100M+ records. Developed Spark, Python, and SQL ETL/ELT pipelines for Epic, Cerner, claims, billing, eligibility, and contact-center sources, improving data freshness to 90-minute SLAs.

AWS S3 Glue EMR Redshift Airflow Snowflake Spark Python SQL DBT Debezium Great Expectations Cloudwatch

Cloud Data Engineer

Sikich

Jun 2016 - May 2018 (1 year 11 months)

Engineered ETL/ELT pipelines on AWS and Azure for 5 enterprise clients across healthcare and financial services, processing 1 TB+ of data daily with 99%+ reliability. Migrated legacy SSIS and on-prem SQL workloads, reducing nightly batch duration by 50%+ and retiring 10+ unsupported servers.