Senior Large-Scale Data Engineer

Nyla Technology Solutions•Annapolis Junction, MD

About The Position

As a Data & Protocol Software Engineer on Team Nyla, you will bring your technical creativity and collaborative energy to build and maintain large-scale data analytics pipelines. Working alongside a team of brilliant, ever-curious engineers, you will solve complex data challenges—combining your deep understanding of network protocols with distributed data processing engines like Spark and Python. Whether optimizing ETL routines, streaming high-volume dataflows, or performing deep protocol analysis, your work will directly empower real-time mission capabilities. If you are passionate about continuous learning, rapid impact, and engineering elegance, you'll fit right in.

Requirements

Hands-on experience building, maintaining, and scaling data processing pipelines handling large-scale datasets.
Proficiency in Python and Apache Spark for data manipulation and distributed computing.
Strong technical background in network architecture and deep protocol analysis.
Bachelor’s Degree in Data Science, Computer Science, Computational Linguistics, Mathematics, or a related technical discipline, PLUS 7+ years of professional experience in data science, NLP, or software engineering OR Master’s Degree in a related field, PLUS 5+ years of relevant professional experience OR High School Diploma / GED , PLUS 11+ years of specialized technical experience in lieu of a degree.
ACTIVE SECURITY CLEARANCE AT THE TS/SCI POLYGRAPH LEVEL IS REQUIRED

Nice To Haves

Practical experience with ETL pipelines, data routing architectures, and dataflow processing (e.g., Apache NiFi).
Exposure to packet processing, low-level inspection tools, or target analysis workflows.
Active involvement in continuous learning, prototype development, or emerging tech exploration.
Outstanding communication skills with a team-first, collaborative mindset.

Responsibilities

Build and maintain large-scale data analytics pipelines.
Solve complex data challenges by combining understanding of network protocols with distributed data processing engines.
Optimize ETL routines.
Stream high-volume dataflows.
Perform deep protocol analysis.