Junior Data Analyst

Design, develop, and maintain interactive dashboards and reports using Power BI, Google Looker Studio (Data Studio), and Tableau to support federal customer program offices. 

Build and manage efficient, secure ETL (Extract, Transform, Load) processes to prepare data for analysis and reporting. 

Create and maintain data models that support accurate, scalable, and reusable visualizations and analytical outputs. 

Apply analytical expression languages (e.g., DAX, Calculated Fields, LOD expressions) to build complex metrics and KPIs within BI platforms. 

Implement and manage row-level data security (RLS) and data sensitivity configurations to ensure compliance with federal data governance standards. 

Collaborate with enterprise data analysts and data engineers to identify, acquire, and integrate data from agency systems and external sources. 

Develop and document end-to-end data preparation workflows, data dictionaries, and technical documentation. 

Transform raw data into clean, structured data models ready for visualization and sharing. 

Deploy and configure BI solutions for consumption by federal stakeholders, ensuring usability and performance. 

Present findings and dashboard outputs clearly to both technical and non-technical audiences, including federal program managers. 

Support recurring and ad-hoc reporting requirements in alignment with program objectives and performance metrics. 

Data Visualization Concepts: Ability to design meaningful, user-friendly visual outputs that communicate complex data simply and accurately. 

Analytical Expression Languages: Proficiency in DAX (Power BI), calculated fields/LOD expressions (Tableau), and/or equivalent formula languages. 

General-Purpose Scripting: Working knowledge of Python or SQL for data wrangling, transformation, and automation. 

End-to-End Data Preparation: Hands-on experience with full data pipeline workflows from raw ingestion through modeling, transformation, and delivery. 

Data Modeling: Ability to create logical and physical data models that support efficient querying and visualization. 

Row-Level Security & Data Sensitivity: Understanding of RLS frameworks in BI tools and sensitivity labeling practices in federal or enterprise environments. 

Data Sharing & Asset Management: Familiarity with publishing, sharing, and governing data assets across organizational workspaces. 

Collaboration: Experience working cross-functionally with data engineers, analysts, and federal clients to define and deliver data solutions. 

Experience collaborating with data engineers or senior analysts to identify and acquire datasets. 

Bachelor's degree in Data Science, Information Systems, Statistics, Business Analytics, Computer Science, or a related field (or equivalent professional experience).