Data Engineer/Machine Learning Developer
Job Category: Information Technology
Time Type: Full time
Minimum Clearance Required to Start: None
Employee Type: Regular
Percentage of Travel Required: Up to 10%
Type of Travel: Local

CACI is currently looking for a motivated, career and customer-oriented Mid-level of Data Engineer and Machine Learning (ML) Developer with Agile methodology experience to join our Customs and Border Protection (CBP) Land Border Integration (LBI) Integrated Traveler Initiative 2.1 (ITI2.1) team in Northern Virginia! Join this passionate team of industry-leading individuals supporting the best practices in Agile Software Development and hardware integration for the Department of Homeland Security (DHS).
As a member of the ITI2.1 Team, you will support the men and women charged with safeguarding the American people and enhancing the Nation’s safety, security, and prosperity. CBP Officers and Border Patrol agents are on the front lines, every day, protecting our national security by combining customs, immigration, border security, and agricultural protection into one coordinated and supportive activity.
CACI agile programs thrive in a culture of innovation and are constantly seeking individuals who can bring creative ideas to solve complex problems, both technical and procedural at the team and portfolio levels. The ability to be adaptable and to work constructively with a technically diverse and geographically separated team is crucial.

The Data Engineer will work with interdisciplinary data teams to design, develop, and deploy machine learning algorithms in ITI2.1 program. The individual will import daily O&M operational and performance outcomes, develop predictive maintenance model to infer and recommend business decision, and/or conduct root cause analysis, support IoT device signal analytics, and trend analysis for ITI2.1 requirement. We are looking for experienced data engineers who know how to solve complex big data problems, work with algorithms, analyze big data and can run end-to-end data analytics.

• Develop an understanding of the customer’s data environment through data profiling, data pipeline, and machine learning/statistical analyses
• Deliver ML software models and components that solve real-world business problems, while working in collaboration with our Product and Data Science teams
• Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment
• Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art, next generation big data and machine learning applications
• Leverage cloud-based architectures and technologies to deliver optimized ML models at scale
• Construct optimized data pipelines to feed ML models
• Use programming languages like Python, Scala, or Java
• Leverage Continuous Integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployments of ML models and application code
• Advocate for software and machine learning engineering best practices
• Function as the engineering tech lead for large-scale initiatives
• Perform statistical analysis and tune using test results
• Study appropriate datasets and transform data science prototypes
• Train data-driven learning model.
• Maintain and work with data pipeline that transfers and processes large scale of heterogenous structural/non-structural data using Spark, Scala, Python, Apache Kafka, TensorFlow, PyTorch, and/or other data analytic tools
• Design, build and support pipelines of data transformation, conversion, validation
• Build data manipulation, processing, and data visualization tools and share these tools across the team.
• Apply data analysis, data mining and data engineering to present data clearly and develop experiments
• Ensure high-quality data and understand how data is generated out experimental design and how these experiments can produce actionable, trustworthy conclusions.
• Work with development teams to build tools for data logging and repeatable data tasks that will accelerate and automate process

The Data Engineer will work with interdisciplinary data teams to design, develop, and deploy machine learning algorithms in ITI2.1 program. The individual will import daily O&M operational and performance outcomes, develop predictive maintenance model to infer and recommend business decision, and/or conduct root cause analysis, support IoT device signal analytics, and trend analysis for ITI2.1 requirement. We are looking for experienced data engineers who know how to solve complex big data problems, work with algorithms, analyze big data and can run end-to-end data analytics.

• Develop an understanding of the customer’s data environment through data profiling, data pipeline, and machine learning/statistical analyses
• Deliver ML software models and components that solve real-world business problems, while working in collaboration with our Product and Data Science teams
• Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment
• Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art, next generation big data and machine learning applications
• Leverage cloud-based architectures and technologies to deliver optimized ML models at scale
• Construct optimized data pipelines to feed ML models
• Use programming languages like Python, Scala, or Java
• Leverage Continuous Integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployments of ML models and application code
• Advocate for software and machine learning engineering best practices
• Function as the engineering tech lead for large-scale initiatives
• Perform statistical analysis and tune using test results
• Study appropriate datasets and transform data science prototypes
• Train data-driven learning model.
• Maintain and work with data pipeline that transfers and processes large scale of heterogenous structural/non-structural data using Spark, Scala, Python, Apache Kafka, TensorFlow, PyTorch, and/or other data analytic tools
• Design, build and support pipelines of data transformation, conversion, validation
• Build data manipulation, processing, and data visualization tools and share these tools across the team.
• Apply data analysis, data mining and data engineering to present data clearly and develop experiments
• Ensure high-quality data and understand how data is generated out experimental design and how these experiments can produce actionable, trustworthy conclusions.
• Work with development teams to build tools for data logging and repeatable data tasks that will accelerate and automate process.