Contractor Support to Data Integration & Decision Support-Enabling Capabilities Portfolio

Alluvionic•Norfolk, VA

17h•Onsite

About The Position

Alluvionic is currently seeking experienced applicants for a Contractor Support to Data Integration & Decision Support-Enabling Capabilities Portfolio position to support the North Atlantic Treaty Organisation (NATO) Headquarters Supreme Allied Commander Transformation (HQ SACT) Capability Development Management Support (CMDS) contract. This role requires a US Citizen with an active NATO or US SECRET clearance (or higher) and is located in Norfolk, VA (On-Site). The position is contingent upon award of the contract.

Requirements

A University Bachelor's degree in Data Science, Computer Science, Information Systems, Engineering, Operations Research, Statistics, Applied Mathematics, or a closely related field; or a minimum of five years of directly relevant professional experience in lieu of the degree.
Directly relevant professional experience shall include work in at least two of the following areas: data integration or data engineering; analytical programming; business intelligence, analytics, or decision-support development.
Demonstrated recent experience personally developing operational data or analytical solutions from identification of a user or decision requirement through data preparation or integration, solution development, user testing, and operational use.
At least twelve calendar months within the last twenty-four months during which hands-on technical delivery was a regular part of the candidate's assigned work.
At least one data or analytical solution personally developed and used by an identified user group as part of an active business or decision-making process within the last eighteen months.
Minimum of three years within the last five years, including hands-on experience within the last twenty-four months, performing data integration or data engineering activities using relational databases, ETL/ELT, APIs, data pipelines, data services, structured data exchange, or comparable methods.
Minimum of two years within the last five years, including hands-on use within the last twenty-four months, using SQL against relational data sources and at least one of Python, R, Scala, Julia, SAS, MATLAB, Stata, or an equivalent general-purpose or statistical analytical programming language to prepare, transform, integrate, automate, analyze, or quality-control data.
Demonstrated experience within the last five years personally designing or developing at least two structured digital data collection, workflow, low-code/no-code, configured-application, or comparable solutions that reduced reliance on manual, email-driven, or spreadsheet-based information processes.
At least one example must have been personally developed or materially enhanced within the last three years.
Minimum of three years within the last five years personally developing business intelligence products, analytical products, dashboards, metrics, visualizations, or comparable decision-support products.
Demonstrated hands-on experience within the last five years, including at least one example within the last three years, applying data modelling, data quality, or data-governance practices as part of an operational data or analytical solution.
Demonstrated experience within the last five years documenting and transferring at least one data, analytical, or digital solution for continued operation or maintenance by another developer, colleague, support team, platform owner, or operational organization.
Minimum of two years within the last five years working directly with operational users, programme personnel, managers, or decision makers to identify information or analytical requirements and communicate findings, assumptions, limitations, risks, or decision implications.

Responsibilities

Support the Enabling Capabilities Portfolio, with an initial emphasis on Infrastructure Programmes, through hands-on data integration, data engineering, analytics, and decision support activities.
Work directly with programme managers, portfolio staff, analysts, data owners, and other stakeholders to understand priority decisions, information requirements, existing data sources, data gaps, and opportunities to improve programme delivery through better use of data.
Design, develop, test, and maintain repeatable data integration processes that ingest, transform, reconcile, and prepare information from multiple programme and enterprise sources for operational use and analysis.
Develop and maintain structured data models, common definitions, business rules, reference data, and data-quality controls required to produce trusted programme and portfolio-level information.
Design, prototype, configure, and improve digital data-collection applications and workflows that replace or materially reduce spreadsheet-intensive, manually consolidated, email driven, or otherwise fragmented data operations.
Support the continued development, maturation, and operational use of digital Management and Oversight Review (MOR) data collection and decision-support solutions for Infrastructure Programmes and other Enabling Capabilities Portfolio requirements.
Develop dashboards, metrics, visualizations, analytical products, and other decision-support solutions that improve visibility of programme status, delivery performance, schedule, resources, risks, dependencies, and emerging issues.
Apply descriptive and diagnostic analytical techniques, and predictive or prescriptive techniques when directed and appropriate to the decision requirement, to identify trends, relationships, anomalies, risks, and opportunities relevant to programme or portfolio management.
Integrate programme and portfolio information with authoritative NATO or enterprise data sources through approved mechanisms including relational databases, APIs, data services, structured file exchange, or other available interfaces.
Identify data-quality, ownership, lineage, governance, stewardship, and authoritative-source issues affecting delivered solutions and implement or recommend practical remediation measures in coordination with responsible stakeholders.
Use appropriate development and engineering practices, including source or configuration control, documented transformations and business rules, reproducible processing, configuration management, and separation of development and operational artifacts where supported by the technical environment.
Document data sources, data models, transformations, business rules, analytical logic, dependencies, assumptions, operating procedures, and solution configuration sufficiently to support transparency, auditability, reuse, sustainment, and transition.
Conduct user testing and iteratively improve solutions based on operational use, stakeholder feedback, data-quality findings, and changes to programme or portfolio information requirements.
Establish appropriate baselines and assess the effect of delivered solutions on factors such as data quality, timeliness, manual processing effort, information availability, workflow efficiency, and usefulness to decision makers.
Present analytical findings, assumptions, limitations, risks, and decision implications to programme managers, portfolio leadership, and other stakeholders.
Support transition of successful prototypes, pilots, data products, and analytical solutions to enduring enterprise platforms, production-support organizations, or designated scaling teams, including preparation of sufficient technical and operational documentation to enable another appropriately qualified individual or team to sustain the solution.