Data Engineer - Junior

Core Responsibilities

• Design end-to-end solution architectures for secure, cloud-based data platforms supporting federal mission workloads, including data ingestion, processing, storage, and analytics layers.
• Develop reference architectures, design patterns, and architectural decision records (ADRs) that guide engineering teams and ensure consistency across the program.
• Architect distributed data processing pipelines using Spark and PySpark, defining partitioning, caching, and resource strategies to optimize throughput and cost.
• Lead database design and data modeling efforts for relational and analytical workloads, including schema design, indexing strategy, and SQL performance tuning.
• Define storage and compute optimization strategies — tiering, right-sizing, autoscaling, and workload placement — to balance performance against budget constraints.
• Collaborate with DevSecOps to embed security and compliance controls (NIST 800-53, FedRAMP, DHS 4300A) into architectural designs and CI/CD workflows using GitHub, Jenkins/CloudBees, Ansible, Terraform, and Vault.
• Conduct architectural reviews, design trade-off analyses, and proof-of-concept evaluations to validate solutions before full implementation.
• Produce architecture documentation, diagrams, and technical briefings tailored to engineering, security, and government stakeholder audiences.
• Provide technical leadership and mentorship to engineering teams, guiding implementation decisions and resolving complex design issues.
• Partner with program leadership to assess emerging technologies, evaluate vendor offerings, and recommend roadmap investments aligned to mission needs.
• Must Be Able to Obtain Public Trust Level 6C
• 3-5 years of Information Assurance experience
• Experience with the following technologies: Python Spark PySpark SQL and Database Implementation Data Engineering and Data Pipelines Storage and Compute Optimization GitHub Jenkins/Cloudbees Ansible Terraform Vault
• Python
• Spark
• PySpark
• SQL and Database Implementation
• Data Engineering and Data Pipelines