Robert Lee Mitchell

Remote senior contributor within Arctiq’s Cloud Data & AI delivery practice, leading client engagements spanning cloud modernization, analytics platforms, and ML enablement across AWS and Azure. Architected and delivered customer-facing lakehouse platforms on Databricks + Delta Lake, establishing standardized ingestion, transformation, and consumption layers for enterprise deployments. Designed end-to-end ETL/ELT workflows to ingest data from SaaS platforms, relational databases, APIs, and event streams into cloud data platforms, supporting analytics and downstream ML use cases. Produced reusable data platform reference architectures covering network isolation patterns, security baselines, CI/CD conventions, and environment promotion strategies. Implemented Databricks Unity Catalog governance (catalogs/schemas, grants, lineage, centralized access controls) and integrated identity via SSO/SCIM. Automated multi-environment infrastructure provisioning with Terraform, aligning deployment

Supported analytics and AI initiatives for government healthcare programs, building data pipelines for Medicaid/Medicare and population health reporting and modeling. Contributed to cloud-native platform modernization on AWS and GCP, replacing legacy on-prem ETL workflows with scalable, managed services. Developed large-scale ingestion and orchestration pipelines using AWS S3, Glue, Lambda, and Step Functions to process claims, eligibility, provider, and encounter data. Designed and implemented GCP analytics workflows with BigQuery, Cloud Storage, and Dataflow to enable reporting, ad hoc analysis, and data science experimentation. Built feature engineering pipelines to support ML use cases such as risk stratification, utilization forecasting, and care management prioritization. Implemented batch scoring workflows and data delivery patterns to publish predictions to downstream case management and reporting systems used by clinical and program teams. Partnered with data science teams to

Delivered data engineering solutions within Presidio’s data and analytics consulting practice for enterprise clients across finance, healthcare, and commercial sectors. Supported early cloud data adoption initiatives, extending on-premises data warehouse capabilities into AWS and GCP for analytics and reporting. Built hybrid ingestion pipelines integrating SQL Server, Oracle, and flat-file sources with Amazon S3 and Google Cloud Storage. Implemented AWS analytics patterns using S3, EC2, Lambda, and Redshift to enable scalable reporting without expanding on-premises infrastructure. Developed BigQuery-backed reporting datasets to accelerate client adoption of GCP analytics and reduce dependency on legacy SQL Server reporting stacks. Designed and maintained dimensional models (star/snowflake) to support BI tools including Tableau, Power BI, Cognos, and MicroStrategy. Tuned ETL and reporting performance through indexing strategies, query refactoring, and batch window optimization to impr