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Information Technology 🏢 Full Time ⭐️ Verified

Federal Data Analyst - Remote Friendly

Department of the Interior, Bureau of Land Management
Anchorage
Estimated Salary
USD 85.000 – USD 110.000
New
Live Update
14 September 2026
Deadline
14 Sep 2027

Job Description

Join our mission-critical team as a Federal Data Analyst in Anchorage, Alaska. This remote-friendly position offers the unique opportunity to serve public interests while leveraging cutting-edge data analytics to drive impactful decisions. You'll work with sensitive federal datasets, ensuring compliance with government standards while delivering actionable insights that shape land management policies across Alaska's vast landscapes.

We offer a competitive compensation package, flexible remote work arrangements, and unparalleled professional development opportunities. Help us preserve America's natural resources through data-driven excellence.

Responsibilities

  • Analyze complex federal datasets related to land use, wildlife conservation, and resource management
  • Develop interactive dashboards and reports using SQL, Python, and Tableau for government stakeholders
  • Ensure data integrity and compliance with federal privacy regulations (FISMA, HIPAA)
  • Collaborate with cross-agency teams to support environmental impact assessments
  • Create predictive models for climate change effects on federal lands
  • Present findings to senior officials and congressional committees

Qualifications

  • Bachelor's degree in Data Science, Statistics, Environmental Science, or related field
  • 3+ years of experience with federal datasets and government reporting standards
  • Advanced proficiency in SQL, Python (Pandas), and data visualization tools
  • Strong understanding of GIS mapping and spatial analysis techniques
  • Certification in federal data security protocols (e.g., FedRAMP)
  • Ability to handle sensitive information with utmost discretion

Required Skills

SQL Python Tableau GIS Data Visualization Federal Compliance Predictive Modeling Statistical Analysis

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