Lead Data Engineer – Secure, Distributed Data Systems

Two-Six-Technologies; Arlington (VA); On-site; USD 140,000 - 170,000; Full time

Job summary

Two Six Technologies is seeking a Lead Data Engineer to join a cross-functional team in Arlington, VA, advancing mission tracking tech with AI/ML. You will design, implement, and support a secure data sharing framework across multiple platform instances.

You will work with Python/Go in highly distributed environments, optimize PostgreSQL schemas, and leverage Elasticsearch, Kafka, Redis, Kubernetes, AWS, and Docker to scale data systems in cloud and on-premise setups.

Qualifications

• Bachelor's degree or comparable experience.
• 6+ years of software development with Python or Go in distributed environments.
• 4+ years designing/optimizing relational schemas in PostgreSQL and using Elasticsearch for indexing.
• 3+ years building event-driven architectures with Kafka and caching with Redis.
• Eligibility to obtain and maintain a TS security clearance.

Responsibilities

• Design and support a seamless, decentralized, secure data sharing framework across platform instances.
• Align storage strategies across relational, indexing, and time-series engines for fast retrieval and persistence.
• Maintain multi-domain data models across dozens of microservices with strict data contracts.
• Scale data systems across bare-metal and cloud environments with infrastructure teams.
• Design resilient data patterns (CQRS, secure store-and-forward) for security and integrity in distributed microservices.

Skills

Python

Go

Education

Bachelor's degree in Computer Science or related field

Tools

PostgreSQL

Elasticsearch

Kafka

Redis

Kubernetes

AWS

Docker

Job description

Two Six Technologies is seeking a Lead Data Engineer to join a cross-functional team in Arlington, VA, advancing mission tracking tech with AI/ML. You will design, implement, and support a secure data sharing framework across multiple platform instances.

You will work with Python/Go in highly distributed environments, optimize PostgreSQL schemas, and leverage Elasticsearch, Kafka, Redis, Kubernetes, AWS, and Docker to scale data systems in cloud and on-premise setups.