Analytics Engineer

V2X•Orlando, FL

1d•Hybrid

About The Position

This role is ideal for a high-potential, early-career data professional who wants to grow into an Analytics Engineer role. You will start with foundational analytics work—cleaning data, building basic models, and supporting dashboards—while being mentored by senior team members. This is a hybrid position that requires the employee to work on-site three days per week.

Requirements

Bachelor’s degree in quantitative or related discipline (e.g., Data Analytics, Statistics, Mathematics, Computer Science, Finance, Supply Chain, Industrial Engineering) or equivalent hands-on experience.
At least 3 years of experience in a data related role or strong portfolio of projects.
Experience with (some or all) regression analysis, Clustering/Classification models, ML algorithms, and Gradient Boosted Tree models or any forecasting or optimization projects.
Ability to demonstrate proficiency in: SQL for queries, joins, common table expressions and window functions.
Microsoft Excel for basic analysis (lookup functions, pivots, etc.).
PowerBI
Familiarity with at least one analytical or scripting language (Python preferred) for data wrangling and EDA.
Ability to build basic dashboards or reports in a BI tool (e.g., Power BI(preferred), Tableau, or similar).
Must possess or be able to obtain and maintain a Common Access Card (CAC) through the National Agency Check and Inquiries (NACI) process.
U.S. Citizenship required for security clearance eligibility.
Demonstrated curiosity and desire to learn advanced analytics, data science, or AI techniques.
Strong analytical mindset.
Comfortable working with messy, real-world data.
Able to interpret trends and communicate findings clearly in writing and presentations.

Nice To Haves

Familiarity with the SDLC is a nice-to-have.
Knowledge of .NET, Angular, HTML/CSS are nice-to-have.
Experience with application frameworks such as Flask or Shiny for Python are nice-to-have.

Responsibilities

Help build and maintain data assets that support Finance (forecasting, variance analysis, program financial health) and Procurement / Supply Chain (cycle time, supplier performance, inventory and readiness).
Over time, be exposed to increasingly complex modeling (forecasting, optimization, risk scoring) and modern data practices.
Data Foundations & Preparation (25%)
Dashboards, Reporting & Automation (25%)
Finance Analytics Support (20%)
Procurement & Supply Chain Analytics Support (20%)
Learning & Growth into Data Science / AI