Database Developer

The Sponsors data analytics and collection services provide intelligence products to inform operations, drive collection, solve Sponsor business problems, and support national security needs. The Sponsor specializes in development and maintenance of large-scale innovative solutions to solve the Sponsor's leading challenges in data correlation, aggregation, and graphing.

Critical Core Competencies

Design, develop, implement and maintain NoSQL databases; utilize expert knowledge of SQL Language to perform database tasks. Translate business requirements into databases, data warehouses, and data streams; create procedures and procedures to ensure data accuracy and accessibility and process, clean, integrate the data. Analyze, plan, and define data architecture framework, including security, reference data, metadata, and master data. Write and engage with server side APIs and applications using existing sponsor data pipelines, application APIs, services or self-written data pipelines, applications and APIs. Gather data requirements such as how long the data needs to be stored, how it will be used and what people and systems need access to the data. Process data for specific needs using tools that access data from different sources, transform and enrich the data, summarize the data and store the data in the storage system.

Required Skills and demonstrated Experience

The Contractor team shall ensure, for the duration of the contract, that contractor personnel assigned to work under this contract maintain institutional knowledge and competency level necessary for all required skills, to include demonstrated on-the-job experience.Skills identified may be needed for O&M of current system along with identification of new technologies and skills to engineer a graph database application for a high security accreditation and approved enhancements.

The Contractor Team shall possess and provide the following required skills and demonstrated experience:

• Demonstrated experience with designing cloud-native architectures using cloud services such as AWS, Google, IBM, and Oracle.
• Demonstrated experience designing and operating big data systems.
• Demonstrated experience building and optimizing performance of large scale graph databases (tens of billions of edges) using DynamoDB or new enhanced capabilities.
• Demonstrated experience developing and operating graph traversal capabilities using data graphing tool traversal capabilities built upon Apache Gremlin or new enhanced capabilities.
• Demonstrated experience developing and operating NoSQL solutions to complex big data applications.
• Demonstrated experience in data modeling for performance, partition sharding, record/event aggregation workflows, stream processing, and metrics gathering.
• Demonstrated experience designing and operating large-scale serverless geospatial indexes built with GeoMESA.
• Demonstrated experience with partition and sort key design and implementation to ensure consistent performance.
• Demonstrated experience with aggregation operations to de-duplicate records on continuous data feeds.
• Demonstrated subject matter expertise experience with relational databases to noSQL.
• Demonstrated experience building and operating high performance data processing pipelines using Lambda, Step Functions and PySpark.
• Demonstrated experience building high quality User Interface/User experiences with the React framework and webGL.
• Demonstrated experience designing and operating large scale graph databases using Apache Cassandra.
• Demonstrated experience performing in-depth technical analysis of large-scale graph databases to develop implementation strategies for search optimizations.
• Demonstrated experience developing technical capabilities for processing, persistence and search of datasets that are collected or maintained using standards common in the Sponsor's community.
• Demonstrated experience facilitating engineering discussions across teams representing multiple stakeholders to develop and execute implementation strategies that meet mission needs.
• Demonstrated experience developing Machine Learning Operations (MLOps) pipelines for large scale application.
• Demonstrated experience maintaining configuration of software using configuration management resources such as GitHub.
• Demonstrated experience designing, building and operating big data systems, such as persistence, partitioning, indexing, at scale of trillions of records/events.
• Demonstrated experience with Niagara Files (NiFi) applications or new enhanced capabilities.
• Demonstrated experience developing and operating Kubernetes infrastructure.
• Demonstrated experience supporting engineering efforts that will contribute to delivery of capabilities such as datasets and functionality such as communications, geospatial workflows.
• Demonstrated experience implementing DevSecOps and agile development in production environments.
• Demonstrated experience with agile software development and testing.
• Demonstrated experience with federal security, regulatory and compliance requirements and security accreditation package development.
• Demonstrated experience with data security and governance using centralized security controls like LDAP, encrypting the data, and auditing access to the data.
• Demonstrated experience with specialized technologies that are optimized for the particular use of the data, such as relational databases, a NoSQL database (Cassandra), or object storage.
• Demonstrated experience with Apache, TINKERPOP, GREMLIN and/or JANUSGRAPH to design, develop, implement and maintain system
• Demonstrated knowledge of Graph Database to design, develop, implement and maintain system.
• Demonstrated experience with C or C to write interfaces.
• Demonstrated experience using centralized security controls like LDAP, encrypting data, and auditing access to data.
• Demonstrated experience with:

Databases: Postgres, MariaDB, ELK, Minio, AWS S3, Neo4j, MongoDB, noSQL

Languages: Python (pypi libraries)

Operating Systems: Centos7, RockyLinux8

Orchestration: Kubernetes, Docker, Docker-Compose, Docker-Swarm

Development Tools: vscode, gitlab, jupyterhub/notebooks, MATLAB

Environments: large collaboration and development environments

Data types: Unstructured, structured, or semi-structured data, including: CSV, JSON, JSONL, AVRO, Protocol Buffers, Parquet, etc.