Data Integration & Decision Support Analyst (N2701)

SimIS Inc

Norfolk (VA)

On-site

USD 90,000 - 130,000

Full time

3 days ago

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Benefits offered by this job

Medical, Dental, Vision

Life Insurance

401(k) Savings Plan

Flex Spending Accounts

Tuition Assistance Program

Professional Development

PTO

Federal Holidays

Job summary

SimIS Inc. seeks a Data Integration & Decision Support Analyst to transform fragmented data into structured, trusted information for program and portfolio decisions. You will design, configure, develop, test, and improve data solutions in collaboration with operational users.

You will work onsite in support of infrastructure programmes, emphasizing data integration, analytics, and decision-support deliverables for mission-driven outcomes.

Qualifications

Master’s degree in Data Science, CS, IS, Engineering or Statistics.
Recent end-to-end hands-on solution delivery in active decision-making within last 18 months.
Data integration and engineering using relational databases, ETL/ELT, APIs, data pipelines.
SQL with relational data and Python/R/Scala for data preparation/analysis.
Digital data collection/workflow modernization through structured solutions.
BI/decision-support products used by programme or portfolio teams.
Applied data modelling, data quality, and governance in operational solutions.
Transition, documentation, and sustainment with version-control and deployment baselines.
Requirements elicitation and communication with operational users to identify information needs.

Responsibilities

Support the Enabling Capabilities Portfolio with hands-on data integration, data engineering, analytics, and decision-support.
Work with programme managers, analysts, data owners, and stakeholders to identify priority decisions and information gaps.
Design, develop, test, and maintain repeatable data integration processes ingesting data from multiple sources.
Develop and maintain structured data models, definitions, and data-quality controls for trusted information.
Design and improve digital data-collection apps and workflows to replace spreadsheet-driven processes.

Skills

Data integration

Data engineering

SQL

Python

BI & dashboards

Data modelling

Data governance

Requirements elicitation

Stakeholder communication

Version control

Education

Master’s degree in Data Science/CS/IS/Engineering/Statistics

Tools

Git

ETL/ELT

APIs

Job description

Position is 100% Onsite

Who We Are:

Founded in 2007, SimIS Inc. is an innovative information technology solution Veteran Owned Small Business (VOSB) that models future environments, requirements, and capabilities, and then secures the enterprise from internal and external threats compliant with Federal, State, and industry standard governance to ensure client mission success. Our performance standard is “excellence,” with an outcomes-based, quality focus in our services and products, guided by our core values of honesty (in word and deed), relationships (confidence and trust with clients and partners), teamwork (shared goals, mission, and purpose), loyalty (allegiance to our client and team), and importance of others (work and win as a team).

SimIS is currently recruiting for the listed position and is contingent upon award. The position is anticipated to be “on site.”

Job Description:

SimIS is seeking contract support for the capture, development, architecture, analysis, and quality assurance of capability requirements necessary to adapt to future threats and shape long-term military strategy. To support our customer, SimIS seeks a Data Integration & Decision Support Analyst to transform fragmented programme information and manual data operations into structured, trusted, reusable information that improves programme and portfolio decision-making and delivery. The contractor is expected to personally design, configure, develop, test, document, and improve working data and analytical solutions in direct collaboration with operational users.

Experience Required:

Master’s degree in Data Science, Computer Science, Information Systems, Engineering, Operations Research, Statistics, or Applied Mathematics.
Recent End-to-End Hands-On Solution Delivery: Candidate’s resume must demonstrate 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. Must demonstrate user or decision requirement through data preparation or integration, solution development, user testing, and operational use.
Data Integration and Engineering: Two years of experience performing data integration or data engineering activities using relational databases, ETL/ELT, APIs, data pipelines, data services, structured data exchange, or comparable methods. Resume must demonstrate at least one example integrating data from two or more separately administered systems, databases, services, or authoritative data sources.
SQL and Analytical Programming: Candidate’s resume must demonstrate at least one example within the last twenty-four months using SQL against relational data sources and at least one example of Python, R, Scala, Julia, SAS, MATLAB, Stata, or an equivalent programming language to prepare, transform, integrate, automate, analyze, or quality‑control data.
Digital Data Collection and Workflow Modernization: Candidate’s resume must demonstrate experience personally designing or developing 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.
Business Intelligence and Decision Support: Candidate’s resume must demonstrate experience personally developing at least one business intelligence product, analytical product, dashboards, metrics, visualizations, or other decision‑support product used by an identified programme, portfolio, management, or operational user group.
Applied Data Modelling, Data Quality, and Governance: Candidate’s resume must demonstrate at least one example of applying data modelling, data quality, or data‑governance practices as part of an operational data or analytical solution. The candidate shall demonstrate practical application in at least two of the following four areas: (1) development of data models or relationships; (2) development of data definitions, metadata, or common data standards; (3) implementation of validation rules or data‑quality controls; or (4) implementation of data lineage, stewardship, or authoritative‑source controls.
Transition, Documentation, and Sustainment: Candidate’s resume must demonstrate at least one data, analytical, or digital solution for continued operation or maintenance by another developer, support team, platform owner, or operational organization. The candidate must also demonstrate use of Git or equivalent source control; platform‑native solution supporting versioned deployment; or a documented configuration baseline maintained under change control.
Requirements Elicitation and Decision Communication: Candidate’s resume must demonstrate two 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.

Benefits: