Data Engineer

Working Location: Norfolk, USA

Security Clearance: NATO Secret

Language: High proficiency level in English language

DESCRIPTION:

Data science, data analytics and Artificial Intelligence (AI) are increasingly gaining momentum in NATO touching all military and political domains and functional areas. In response to HQ SACT s understanding of the disruptive potential of data science and AI, and recognizing the strategic value of data, the Data Science & Artificial Intelligence section, established in 2020 in the Federated Interoperability Branch, is focusing on data science and AI as cross-cutting and enabling capabilities for HQ SACT and the NATO Enterprise. The section provides a broad spectrum from strategy and policy development and support to technical delivery and implementation to HQ SACT and the NATO Enterprise. In addition to serving as the centre of gravity for HQ SACT s efforts in advancing data centricity and integrating rapidly changing technology related to data exploitation, the section has developed a substantial reputation inside NATO and is regularly invited to offer policy and technical expertise.

EXPERIENCE AND EDUCATION:

Essential Qualifications/Experience:

A Bachelor of Science degree from a recognized university in computer science, IT, software or computer engineering, data science, applied math, physics, statistics, or a related field

A Master s degree or higher from a recognized university in computer science, IT, software or computer engineering, data science, applied math, physics, statistics, or a related field

Experience with advanced level SQL, including query optimization, complex joins, development of stored procedures, user-defined functions and working with Analytic Functions in the last 3 years

Proficient in at least one data manipulation language such as Python, Scala, R, etc.

Ability to develop ETL processes for batch and streaming data, with proficiency in tools and technologies such as Apache Spark, Apache Airflow, Pentaho Data Integration, SQL Server Integration Service

Advanced knowledge of relational database architecture, including design of OLAP and OLTP databases

Experience working with at least one Data Warehouse schemas such as Star or Snowflake

Ability to work with large datasets

Working experience in an international environment with both military and civilian elements

Understanding of the NATO organization and its functions

Desirable Qualifications/Experience:

Knowledge of NoSQL databases such as MongoDB, Cosmo DB

Ability to work in cloud environments to develop scalable data pipelines

Skills in Cloud infrastructure and technologies such as Google Cloud Compute, AWS, Azure Data Factory, distributed computing

Working experience with geospatial data structures such as raster and vector-based data

Ability to collect and document project requirements, and to translate the requirements to technical solutions, including working in an agile environment to implement complex database projects

DUTIES/ROLE:

Contribute to the development and implementation of an enabling data science and AI capability at HQ SACT and for the NATO

Contribute to ML/AI initiatives across HQ SACT and the NATO Enterprise with a particular focus on the data engineering side

Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, proposes how to re-design infrastructure for greater scalability

Develop, construct, test and maintain data pipelines and architectures such as databases and large-scale processing systems, within the constraints of existing but evolving processes and technologies

Transform data into formats that can be easily analyzed by developing, maintaining, and testing infrastructures for data generation

Prepare data for prescriptive and predictive modelling

Provide subject matter expertise to (military and civilian) staff within HQ SACT or the NATO Enterprise and develop proofs of concept, as directed

Work in tandem with data scientists and software engineers

Select from existing data sources and prepare data to be used by data science models

Improve data quality and efficiency

Support evaluation of operational requirements and objectives

Interpret trends and patterns and support building of algorithms and prototypes

Support educational efforts and training development related to data, AI or digital literacy

Remain up-to-date with new developments in data engineering and data architectures to bring innovative ideas into implementation

Support building a data-driven culture that uses data and analytics to generate insights, improve decision making at all levels, inform strategy and policy decisions, and improve performance

Perform additional tasks as required, related to the LABOR category