Lead Data Architect ( R-00062768 )
Come work with some of the most talented and brightest minds at Leidos. We are seeking a Cloud/Data Architect to define, develop, architect and implement a data distribution architecture and management strategy on a project that will define the future of the Army's synthetic training environments across a distributed network. Sign-on bonus and relocation assistance are available to candidates motivated to grow their career in Orlando, FL.
In this role, you will be the primary source of expertise providing technical advice/input to strategic system solutions, detailed subsystem designs, and advice to other disciplines as needed to inform peripheral design decision ensuring system-wide compatibility of the data solutions being considered. You may have oversight of a small team of data engineers and software engineers, and will interact with other external stakeholders to ensure all involved staff are aware of the implications of their decisions and grow in their understanding of the impacts on this program. The successful candidate will be responsible for a data architecture that may grow to encompass peta-bytes of data at enterprise level systems. In this role, you will additionally:
Design, detail, lead, correct and implement a system-level data architecture and management solution addressing the broad data needs of a complex highly distributed system supporting simulated training within the DOD.
Demonstrate the ability to work fluently with structured and unstructured data, including diverse sources of “big data”.
Anticipate the need for Machine Learning, Predictive modeling, Statistical Analysis and Hypothesis testing.
Confront highly complex and multi-dimensional problems integrating computer simulations, virtual 3D displays, multi-tiered control interfaces, distributed computing environments, cloud based data storage, and delivery of services/solutions to a widely dispersed user community.
Support and deployment of ""leap-ahead"" technologies in the field of simulation based training.
Active DoD secret clearance
BS degree and 12 – 15 years of relevant experience or Masters with 10 – 13 years of relevant experience.
Significant relevant data architecture experience as a part of technical implementation of network, server and/or distributed computing experience including Schema formats, storage, evolution and design.
Demonstrated experience designing with and configuring various data processing, storage and transformation tools such as Kafka, logstash, Elastic Search, NiFi, Neo4J, and others.
Proven track record developing on and using advanced software programs compatible with MBSE tools such as Magic Draw and requirements tools such as DOORS. Proven solutions and successes in developing algorithms, queries and automated processes to cleanse, integrate and evaluate the quality of datasets and models in a complex simulation environment is required. Experience in AI and ML is required.
The successful candidate must possess mastery of distributed computing and data access, computer virtualization, network implementation and addressing issues such as latency in displays/system responses.
Prior experience with Confluent Kafka, kSQL including sizing, configuring and tuning Kafka cluster deployments
Prior experience with streaming data architectures and modern development approaches (serverless, containerization, cloud, continuous delivery, micro-services, event-based applications)
Demonstrate 4 years of experience with AI/ML implementation and deployment.
Demonstrated experience in managing interactions with other team members, program leadership, customer leadership and end users.
Cloud and distributed computing background with demonstrated experience in at least 2 different IT/Cloud technical areas.
Demonstrated understanding of employing Big Data solutions compatible with DOD networks.
Demonstrated experience with AWS or similar virtual environments
Published papers dealing with the core technologies being addressed.
Demonstrated 10 years of experience with AI/ML implementation and deployment.