Jobs at Capital Technology Group

Design, secure, and scale cloud infrastructure for federal initiatives. Lead cloud modernization and migration, develop architecture standards, embed cloud security and compliance (NIST/FedRAMP/FISMA/CIS), support IaC, CI/CD, IAM, encryption, and automation, assess risks/resiliency, and collaborate with engineering, DevSecOps, and cybersecurity teams.

Design and lead end-to-end, secure, scalable application and integration architectures for federal modernization projects. Translate requirements into architecture strategies, guide API and cloud designs, evaluate tradeoffs, mentor teams, and ensure solutions meet security, compliance, and performance needs.

Lead design and modernization of secure, scalable backend systems for federal missions. Drive architecture, API design (REST/GraphQL), cloud-native development, CI/CD, DevSecOps, troubleshooting, and mentor engineers to deliver production-ready solutions.

Design, build, and maintain scalable ETL/ELT data pipelines and analytical platforms on AWS and Databricks. Migrate legacy workflows to cloud-native services, implement data lake and catalog solutions, enable AI/RAG/vector search, ensure FedRAMP/NIST compliance, optimize performance, and mentor junior engineers while collaborating in Agile teams.

Design, build, and maintain scalable ETL/ELT pipelines and data models using Databricks, Spark, dbt, SQL, and AWS services. Optimize ingestion, transformation, orchestration, and monitoring across structured and semi-structured data. Collaborate in Agile teams, support analytics and reporting for large-scale federal environments, and evaluate new data tools and platform improvements.

Design, build, and maintain scalable AWS-native data platforms and ETL/ELT pipelines using Databricks, Spark, Python, dbt, and SQL. Implement ingestion, transformation, orchestration, and analytics solutions (Iceberg, Parquet, Redshift, Athena), support FedRAMP/NIST controls, migrate legacy systems to cloud-native services, and mentor junior engineers while collaborating in Agile teams.

Lead QA activities for an IAM program: design and implement QA methodologies, create and run manual and automated tests, perform code/product reviews, triage defects, validate functionality/performance/security, and support CI/CD, UAT, and release validation across Agile teams.

The Lead AI/ML Engineer evaluates emerging technologies, leads teams in adopting solutions, and provides technical leadership while mentoring junior specialists.