I am a Senior Data Engineer and MLOps specialist with deep experience designing and operating multi-cloud data platforms across AWS, Azure, Google Cloud, and Oracle Cloud. I’ve spent over a decade building analytics and machine learning systems in regulated and high-scale environments, turning complex, messy data into reliable, production-grade platforms. My work spans cloud data warehouses, lakehouse architectures, streaming pipelines, and ML feature pipelines, with a strong focus on data quality, security, and operational reliability. I am known for modernizing legacy systems, navigating ambiguity, and partnering closely with analytics, data science, and business teams to deliver data products that are trusted and actually used.

Robert Lee Mitchell

I am a Senior Data Engineer and MLOps specialist with deep experience designing and operating multi-cloud data platforms across AWS, Azure, Google Cloud, and Oracle Cloud. I’ve spent over a decade building analytics and machine learning systems in regulated and high-scale environments, turning complex, messy data into reliable, production-grade platforms. My work spans cloud data warehouses, lakehouse architectures, streaming pipelines, and ML feature pipelines, with a strong focus on data quality, security, and operational reliability. I am known for modernizing legacy systems, navigating ambiguity, and partnering closely with analytics, data science, and business teams to deliver data products that are trusted and actually used.

Available to hire

I am a Senior Data Engineer and MLOps specialist with deep experience designing and operating multi-cloud data platforms across AWS, Azure, Google Cloud, and Oracle Cloud. I’ve spent over a decade building analytics and machine learning systems in regulated and high-scale environments, turning complex, messy data into reliable, production-grade platforms.

My work spans cloud data warehouses, lakehouse architectures, streaming pipelines, and ML feature pipelines, with a strong focus on data quality, security, and operational reliability. I am known for modernizing legacy systems, navigating ambiguity, and partnering closely with analytics, data science, and business teams to deliver data products that are trusted and actually used.

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Experience Level

Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Intermediate
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Language

English
Fluent

Work Experience

Senior Data Engineer at Arctiq
March 1, 2022 - February 1, 2026
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
ML Ops & Big Data Engineer at Acentra Health
February 1, 2018 - February 1, 2022
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
Data Engineer at Presidio
January 1, 2015 - December 1, 2017
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

Education

Bachelor's degree in Computer Science at Texas A&M University
October 1, 2010 - December 1, 2014
Bachelor's degree of Computer Science at Texas A&M University
October 1, 2010 - December 1, 2014

Qualifications

Add your qualifications or awards here.

Industry Experience

Healthcare, Government, Professional Services, Software & Internet, Financial Services