Lead Data Engineer

Two Six Technologies•Arlington, VA

9d•$132,000 - $198,000•Onsite

About The Position

Two Six Technologies is seeking an experienced and motivated Lead Data Engineer to work within a cross-functional development team focused on advancing a state of the art in mission tracking technology enhanced by artificial intelligence and machine learning. The work will be located in our Arlington, Virginia office.

Requirements

Bachelor’s degree in Computer Science or related field or comparable experience
6+ years of professional software development experience, with deep expertise in Python and/or Go (Golang) within highly distributed environments.
4+ years of hands-on experience designing and optimizing relational schemas in PostgreSQL, alongside experience utilizing Elasticsearch for high-performance indexing and retrieval.
3+ years of experience implementing and scaling event-driven architectures with distributed message brokers like Kafka, and leveraging Redis (or similar key-value stores) for high-speed caching.
Eligibility to obtain and maintain a TS security clearance.

Nice To Haves

Exposure to DevSecOps and Agile environments and processes
Proficient understanding of code versioning tools, such as Git
A self-driven work ethic and a willingness to jump in and solve hard problems
Adept at simplifying and communicating complex ideas
Experience with Kubernetes, AWS, Docker, a plus
DoD 8570 Level II or Level III Certification, for example, Comptia Security + or CISSP

Responsibilities

Partner across teams to design and support the implementation of a seamless, decentralized, and secure data sharing framework that ensures consistent data exchange across different platform instances.
Work closely with backend and database teams to align storage strategies across relational, indexing, and specialized time-series engines, balancing fast retrieval with long-term persistence.
Maintain and align complex, multi-domain data models utilized across dozens of microservices, ensuring data integrity, strict contract management, and system-wide consistency.
Collaborate with infrastructure and platform teams to adapt and scale data systems as computational needs evolve, ensuring seamless performance across both bare-metal and cloud environments.
Collaborate on the design and implementation of resilient data patterns (such as CQRS and secure store-and-forward pipelines) to guarantee data security, integrity, and strict access controls in a distributed microservices environment.