Data Platforms Analyst - National Geographic
$85,000–$116,800 year
On-siteWashington, United States
Job Summary
Develop and maintain clean, reusable data models for reporting and forecasting while supporting the build-out of CI/CD and testing practices for data workflows. Translate stakeholder requirements into structured analytics layers and collaborate with business teams to prepare and monitor machine learning features for marketing and retention efforts. Document processes, contribute to governance, and ensure data quality by validating ingested data and reconciling schema drift. This role operates at the intersection of data engineering and applied analytics within National Geographic's subscription business, requiring on-site presence four days per week in Washington, D.C.
Required Qualifications
- 2+ years of experience in data analytics, data operations or marketing analytics, ideally with a subscription, media or digital consumer business
- 2+ years experience in SQL and Python
- Exposure to Spark and Databricks
- Familiarity with dbt or other transformation frameworks
- Experience validating and reconciling ingested data (schema drift, nulls, bad joins)
- Experience with data visualization tools (Tableau, Looker, PowerBI)
- Basic understanding of Git workflows and CI/CD
- Comfort working with cloud data warehouses (Snowflake, BigQuery, Databricks, or similar)
- Excellent communication skills and ability to explain technical findings to non-technical partners
- Demonstrated ability to manage multiple projects and meet deadlines in a fast-paced environment
- Candidates must be based within a reasonable commuting distance of our Washington, D.C. office, as this role requires on-site presence four days per week
Desired Qualifications
- Bachelor's degree in Quantitative field (e.g. Statistics, Analytics, Mathematics, Engineering) , or equivalent/related degree is preferred
- Working knowledge of Fivetran/Airbyte or other ELT tools (or at least willingness to learn)
- Familiarity with SaaS APIs
- Interest in ML/AI and willingness to learn model deployment practices
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