Data Engineer
Data Engineer Belong. Connect. Grow. with KBR! KBR's National Security Solutions team provides high-end engineering and advanced technology solutions to our customers in the intelligence and national security communities. In this position, your work will have a profound impact on the country's most critical role - protecting our national security. Why Join Us?
- Innovative Projects: KBR's work is at the forefront of engineering, logistics, operations, science, program management, mission IT and cybersecurity solutions.
- Collaborative Environment: Be part of a dynamic team that thrives on collaboration and innovation, fostering a supportive and intellectually stimulating workplace.
- Impactful Work: Your contributions will be pivotal in designing and optimizing defense systems that ensure national security and shape the future of space defense.
- Analytic Experience: Candidate will be a part of the technical team responsible for providing analytic consulting services, supporting analytic workflow and product development and testing, promoting the user adoption of methods and best practices from data science, conducting applied methods projects, and supporting the creation of analysis-ready data.
- Onsite Support: Candidate will be the face of the CHEETAS Team and will be responsible for ensuring stakeholders have the analytical tools, data products and reports they need to make insightful recommendations based on your data driven analysis.
- Stakeholder Assistance: Candidate will directly assisting both analyst / technical and non-analyst / non-technical stakeholders with the analysis of DoD datasets and demonstrating the 'art of the possible' to the stakeholders and VIPs with insights gained from your analysis of DoD Test and Evaluation (T&E) data.
- Communication: Must effectively communicate at both a programmatic and technical level. Although you potentially may be the only team member physically on-site supporting you will not be alone. You will have support from other data science team members as well as the software engineering and system administration teams.
- Technical Support: Candidate will be responsible for running and operating CHEETAS (and other tools); demonstrating these tools to stakeholders & VIPs; conveying analysis results; adapting internally-developed tools, notebooks and reports to meet emerging needs; gathering use cases, requirements, gaps and needs from stakeholders and for larger development items providing that information as feature requests or bug reports to the CHEETAS development team; and performing impromptu hands-on training sessions with end users and potentially troubleshooting problems from within closed networks without internet access (with support from distributed team members).
- Independent Work: Candidate must be self-motivated and capable of working independently with little supervision / direct tasking.
- Location: Onsite; Honolulu, HI
- Travel Requirements: This position will require travel of 25% with potential surge to 50% to support end users located at various DoD ranges & labs located across the US (including Alaska and Hawaii). When not supporting a site, this position can work remotely or from a nearby KBR office ( if available and desired ).
- Working Hours: Standard, although you potentially may be the only team member physically on-site providing support, you will not be alone.
- Security Clearance: Active or current TS/SCI Clearance is required
- Education: A degree in operations research, engineering, applied math, statistics, computer science or information technology with preferred 15+ years of experience within DoD. Candidates with 10-15 years of DoD experience will be considered on a case-by-case basis. Entry level candidates will not be considered.
- Technical Experience: Previous experience must include five (5) years of hands-on experience in big data analytics, five (5) years of hands-on experience with object-oriented and functional languages (e.g., Python, R, C++, C#, Java, Scala, etc.).
- Data Experience: Experience in dealing with imperfections in data. Experience should demonstrate competency in key concepts from software engineering, computer programming, statistical analysis, data mining algorithms, machine learning, and modeling sufficient to inform technical choices and infrastructure configuration.
- Data Analytics: Proven analytical skills and experience in preparing and handling large volumes of data for ETL processes. Experience should include working with teams in the development and interpretation the results of analytic products with DoD specific data types.
- Big Data Infrastructure: Experience in the installation, configuration, and use of big data infrastructure (Spark, Trino, Hadoop, Hive, Neo4J, JanusGraph, HBase, MS SQL Server with Polybase, VMWare as examples). Experience in implementing Data Visualization solutions.
- Experience using scripting languages (Python and R) to process, analyze and visualize data.
- Experience using notebooks (Jupyter Notebooks and RMarkdown) to create reproducible and explainable products.
- Experience using interactive visualization tools (RShiny, pyShiny, Dash) to create interactive analytics.
- Experience generating and presenting reports, visualizations and findings to customers.
- Experience building and optimizing 'big data' data pipelines, architectures and data sets.
- Experience cleaning and preparing time series and geospatial data for analysis.
- Experience working with Windows, Linux, and containers.
- Experience querying databases using SQL and working with and configuring distributed storage and computing environments to conduct analysis (Spark, Trino, Hadoop, Hive, Neo4J, JanusGraph, MongoDB, Accumulo, HBase as examples).
- Experience working with code repositories in a collaborative team.
- Ability to make insightful recommendations based on data driven analysis and customer interactions.
- Ability to effectively communicate both orally and in writing with customers and teammates.
- Ability to speak and present findings in front of large technical and non-technical groups.
- Ability to create documentation and repeatable procedures to enable reproducible research.
- Ability to create training and educational content for novice end users on the use of tools and novel analytic methods.
- Ability to solve problems, debug, and troubleshoot while under pressure and time constraints is required.
- Should be self-motivated to design, develop, enhance, reengineer or integrate software applications to improve the quality of data outputs available for end users.
- Ability to work closely with data scientists to develop and subsequently implement the best technical design and approach for new analytical products.
- Strong analytical skills related to working with both structured and unstructured datasets.
- Excellent programming, testing, debugging, and problem-solving skills.
- Experience designing, building, and maintaining both new and existing data systems and solutions
- Understanding of ETL processes, how they function and experience implementing ETL processes required.
- Knowledge of message queuing, stream processing and extracting value from large disparate datasets.
- Knowledge of software design patterns and Agile Development methodologies is required.
- Knowledge of methods from operations research, statistical and machine learning, data science, and computer science is sufficient to select appropriate methods to enable data preparation and computing architecture configuration to implement these approaches.
- Knowledge of computer programming concepts, data structures and storage architecture, to include relational and non-relational databases, distributed computing frameworks, and modeling and simulation experimentation sufficient to select appropriate methods to enable data preparation and computing architecture configuration to implement these approaches.
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