Data Engineer with Security Clearance

3 weeks ago

Liberty, United States Top Stack Full time

TS Clearance Required Minimum of 2-year experience is required
Bachelor's degree in a STEM field with preference towards Computer Science and Software Engineering.
Verifiable work experience working with data structures, database management, distributed computing, and API driven architectures using SQL and No-SQL engines.
A Certified Data Management Professional certification is preferred.
Proficient in modeling frameworks like Universal Modeling Language (UML), Agile Development, and Git Operations.
At least 'Interim Secret' w/in 30 days of hire is required, while Top Secret clearance eligible for SCI is preferred. A data engineer has a deep understanding of performance optimization and data pipelining. In addition to the baseline skills of a data analyst, data engineers can make raw data more useful for the enterprise. Data engineers can create and integrate application programming interfaces (APIs). Their technical skills generally include multiple programming languages and a deep knowledge of SQL database design.
The data engineer role requires a more in-depth knowledge in programming for integrating complex models and using advanced software library frameworks to distribute large, clustered data sets. Data engineers collect and arrange data in a form that is useful for analytics. A basic knowledge in machine learning is also required to build efficient and accurate data pipelines to meet the needs for downstream users such as data scientists to create the models and analytics that produce insight.
The Data Engineer shall perform the following tasks:
Developing, maintaining, and testing infrastructures for data generation to transform data from various structured and unstructured data sources.
Develop complex queries to ensure accessibility while optimizing the performance of NoSQL and or big data infrastructure. Create and maintain optimal data pipeline architecture.
Build and maintain the infrastructure to support extraction, transformation, and loading (ETL) of data from a wide variety of data sources. Extract data from multiple data sources, relational SQL and NoSQL databases, and other platform APIs, for data ingestion and integration.
Configure and manage data analytic frameworks and pipelines using databases and tools such as NoSQL, SQL, HDInsight, MongoDB, Cassandra, Neo4j, GraphDB, OrientDB, Spark, Hadoop, Kafka, Hive, and Pig.
Apply distributed systems concepts and principles such as consistency and availability, liveness and safety, durability, reliability, fault-tolerance, consensus algorithms.
Administrate cloud computing and CI/CD pipelines to include Azure, Google, and Amazon Web Service (AWS).
Coordinate with stakeholders, including product, data and design teams to assist with data-related technical issues and support their data infrastructure needs

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