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Director of Data Integration and Engineering ( R-00066518 )

Engineering and Architecture

Architectural Director



Arlington, Virginia, United States


Job Description:

The Leidos AIML Accelerator is seeking a Director of Data Integration and Engineering who will serve as a senior leader within our organization and play a key role in the design and execution of data strategy across Leidos. You will work with other senior leaders in the Accelerator, the Office of Technology (OoT), and across Leidos to accelerate data-driven innovation by developing and refining strategy; developing solution patterns for the Enterprise; leading AI activities within major captures and programs; and representing Leidos capabilities data engineering and data integration to our customers, our internal and external partners, and the community. You will lead and grow a talented and dedicated team of data engineer who are developing and delivering solutions broad range of the most critical and strategic data challenges across Leidos.

The Director of Data Integration will lead the formulation and execution of an ambitious research and investment agenda that focuses on delivering key capabilities in data integration and data engineering to our customers and markets. In collaboration with senior leaders in the Accelerator and across Leidos, he/she will refine and expand a Leidos Enterprise strategy in AI that supports our markets in Health, Civil, Defense, and Intelligence. You will lead the selection, design, and delivery of scalable solution patterns that enable rapid development of new solutions across Leidos pipeline and programs.

- Be a passionate advocate of the power and importance of data, dedicated to unleashing the power of data to enable advanced analytics and AI across a $12B Leidos portfolio of major programs in in Defense, Intelligence, Civil, and Health.
--Expand and enhance data integration and engineering strategy and broader data strategy at Leidos, focusing on how we will deliver secure data solutions at speed and scale by combining our own technology with leading commercial and open source technologies.
- Own relationships with diverse stakeholders across Leidos and our customer base to understand current and anticipate future business needs, understand requirements and define relevant strategic questions
- Champion the application, usage and adoption of data governance across customer and enterprise data, including data cataloguing, data discovery, data lineage, privacy & security frameworks, data quality, and data enrichment.
- Design, articulate, and lead solution development across Leidos consistent with this strategy to support a large pipeline of opportunities in Intelligence, Defense, Civil, and Health markets for which these technologies are critical.
-Lead and grow a team of ~10 data engineers, developers, and solution architects within the Leidos AI / ML Accelerator developing and delivering data solutions in a range of technology areas, from IT Ops to Cybersecurity to Natural Language Processing (NLP) to computer vision.
- Lead partnership activities with key Leidos suppliers in key horizontal (AI/ML, data integration and management, cloud computing) and vertical (AI Ops, cybersecurity, health, space, intelligence) markets.
-Serve as a leading technical expert on data engineering and integration within Leidos and across the community, including:
• Supporting the most strategic captures across all Leidos business areas by identifying opportunities to leverage data engineering and AI as part of an overall solution that delivers value to the customer, designing the specific solution elements capable of delivering that value, and articulating that solution within effective and compelling proposals.
• Advising C-suite and Leidos business areas leaders on a broad range of technology, strategy, and policy issues associated with data engineering and integration.
• Identifying and supporting M&A activities related to data engineering and integration.
• Representing Leidos as a member of external advisory groups in data engineering, AI and related areas.

This is you:
-Advanced degree (MS desired) in computer science or a related discipline, such as data science, information systems, applied mathematics or statistical analysis, electrical engineering, or systems engineering.
-10+ years of technical leadership within data-centric software programs in industry, academia, or government, including at least 5 years of technical leadership within large-scale ($25M+) data engineering/data integration programs.
2+ years leading / overseeing Agile SW development. Experience leading Agile development in a complex, matrixed organization with many interdependencies is strongly preferred.
-Thorough knowledge and understanding of many areas of modern approaches for big data processing and management including (but not limited to) databases, data lakes, data pipelines, data governance, data integration, data engineering, and data science for:
*Data storage & modeling (HDFS, ElasticSearch, etc.)
* Data/message streaming and orchestration (Kafka, NiFi, SQS, etc.),
* Cloud / hybrid / On-prem deployment architectures
* Structured and unstructured data* Relational and distributed data models
* SQL and NoSQL databases
* Massive (PB+) datasets
* Secured data (PHI, CUI, classified, etc.)
- Proven ability to design, articulate, and deliver complex, large-scale data solutions that:
* are scalable, robust, secure, and resilient
* deliver clear, measurable value to program owners and end users.
-Ability to communicate effectively about data integration and data engineering with a broad range of audiences, from internal leaders to external customers and from sophisticated researchers to those with little or no relevant background.

-TS/SCI preferred. The ability to obtain a TS/SCI clearance is strongly preferred.
-Established relationships with leading data and cloud-computing companies.
-Experience in designing solutions that optimize data systems performance and scalability.
-Familiarity with a broad range of commercial and Open-Source data engineering/data integration and AI / ML / Data Science platforms and awareness of emerging technology and key trends within the field.
-Hands-on experience with a broad range of current AIML tools (e.g., TensorFlow, Spark, Python, PyTorch, Pandas) and collaboration environments (e.g. Jupyter notebooks)



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