Bachelor's degree in Computer Science, Mathematics, Electrical Engineering, or related field.
9+ years of experience with a Bachelor's, 7+ years with a Master's, or 4+ years with a PhD/JD.
Active TS/SCI clearance required.
3+ years in data engineering, data operations, or DevOps roles.
Proficient in Python, Bash, or similar scripting languages.
Familiar with Linux (RedHat) command line and Windows environments.
Solid understanding of data engineering fundamentals and data quality principles.
Responsibilities
Design and maintain real-time data ingestion pipelines using Apache NiFi and REST APIs.
Monitor data quality and health of data ingestion processes.
Ensure compliance with data transfer policies across security domains.
Transform and validate structured and unstructured data formats.
Utilize data visualization tools to report on pipeline status and trends.
Collaborate with engineering teams to enhance pipeline reliability.
Automate monitoring and alerting for data feed issues.
Benefits
On-site position in Honolulu, HI.
Opportunity to work on mission-critical systems.
Collaboration with cross-functional teams in a dynamic environment.
Engagement with cutting-edge data technologies and tools.
Full Job Description
Job Description
We are seeking a Big Data Engineer to design, build, and maintain real-time and near-realtime data ingestion pipelines supporting a mission-critical system and moves data across security domains using cross domain solutions (CDS) / guards. You will be responsible for the reliable flow of streaming data from a wide range of sources into our data platform, ensuring data quality, observability, and scalability.
You'll partner closely with data engineers, platform engineers, analytics teams, and security/governance personnel to deliver trustworthy, low-latency data that supports operational decisions - while ensuring compliance with cross-domain and data classification requirements. This position is on-site in Honolulu, HI.
Key Responsibilities
Aid the team in delivering continual data feeds to users and monitoring the health of data quality and overall data ingest.
Design, build, and maintain resilient real-time/near-realtime ingestion pipelines using Apache NiFi and REST APIs, with appropriate backpressure, prioritization, retries, and error-handling strategies.
Configure and monitor data flows moving across security domains via cross domain guards/solutions, ensuring data integrity and compliance with transfer policies.
Parse, transform, validate, and route structured and unstructured data in a variety of formats, including Excel, CSV, JSON, and XML.
Employ data manipulation and visualization tools (e.g., Grafana, Prometheus) to effectively convey pipeline status, data quality, and historical trends to leadership, users, and the data team.
Collaborate with platform, software, and other data engineers to (re)configure and continuously improve data ingestion pipeline reliability.
Develop and maintain software/scripts to automate monitoring of real-time feeds and alert on timeliness, volume, lineage, and distribution data issues.
Translate learnings from historical pipeline data into actionable steps to improve data ingest reliability and performance.
Partner with security and governance teams to enforce encryption, authentication, authorization, and data classification requirements across pipelines and cross-domain transfers.
Write and maintain scripts (Python, Bash, or similar) to automate data processing, validation, and monitoring tasks.
Document data flows, system configurations, and standard operating procedures.
Qualifications
TYPICAL EDUCATION AND EXPERIENCE: Bachelors and nine (9) years or more experience; Masters and seven (7) years or more experience ; PhD or JD and four (4) years or more experience.
U.S. Citizenship and an active TS/SCI clearance.
Bachelor of Science degree in Computer Science, Mathematics, Electrical Engineering, Physics, Information Systems, Information Technology, or related field.
3+ years of experience in data engineering, data operations, or DevOps roles supporting production data pipelines.
Proficiency in Python, Bash, or similar scripting languages commonly used in data science/data analytics applications.
Working knowledge of the Linux (RedHat) command line; familiarity with Windows environments.
Solid understanding of data engineering fundamentals: data pipelines, streaming architectures, ETL/ELT concepts, and data quality principles.
Familiarity with JSON, XML, CSV, and Excel data formats, parsing, and transformation.
Preferred Qualifications (Nice to Have)
Experience with streaming/messaging platforms and tools: Kafka, JMS, Apache Flink/Spark Streaming.
Experience with monitoring/observability tools: Grafana, Prometheus, Elasticsearch.
Experience with data platforms such as Snowflake.
Familiarity with cross domain solutions/guards (e.g., data diode concepts, transfer validation, content filtering).
Experience enforcing encryption, authentication/authorization, and data classification policies in partnership with security/governance teams.
Experience with version control tools (e.g., Git) and Agile/Scrum practices.
Prior experience in a government, defense, or intelligence community environment.
About SAIC
Science Applications International Corporation (SAIC) is a technology integrator in the technical, engineering, intelligence, and enterprise information technology markets. SAIC has approximately 26,000 employees and operates in more than 70 countries. The company was founded in 1969 and is headquartered in Reston, Virginia. SAIC provides services to the U.S. government, including the Department of Defense, the intelligence community, and civilian agencies. The company also serves commercial customers in the healthcare, energy, and financial services sectors.
