Research & Data Analyst developing robust data solutions and analytics at innovative company in Arlington. Collaborating with cross-functional teams and ensuring data integrity.
Responsibilities
Identify and Onboard New Data Sources: Lead exhaustive research to identify, evaluate, and obtain new OSINT sources. Establish and maintain relationships with third-party data providers to continuously expand platform capabilities.
Perform Strategic Research and Analysis: Conduct exhaustive research and advanced analysis, including writing SQL for quantitative conclusions and leading the qualitative analysis of unstructured sources (e.g., program documents) to extract nuance, intent, and context. This work establishes analytical frameworks, enables evidence-based decisions, and sets the foundation for all product features.
Clean and Structure Data for Production: Profile, validate, and work with engineering to transform raw, complex data into standardized formats that integrate seamlessly with our existing infrastructure and support automation.
Build and Maintain Data Workflows: Create and document repeatable processes for efficient data ingestion, comprehensive quality checks, and maintenance, ensuring the system can scale.
Create and Maintain a Data Audit Structure: Design and implement robust data checks within existing frameworks, performing cross-source comparisons to ensure absolute accuracy and data integrity.
Collaborate Across Teams: Serve as the go-to data domain expert, working closely with Product, Engineering, and Customer Success to translate customer needs into robust data solutions and inform the product roadmap. This includes creating quick-turnaround dashboards to test insights with real users before productization.
Document data assets — Create and maintain data dictionaries, lineage documentation, and process guides that enable teams to understand and use data effectively
Requirements
2-4 years of experience working with data in a research, analyst, or business intelligence role
Strong SQL skills — Comfortable writing queries to join, filter, aggregate, and analyze data across multiple tables and sources
Data quality mindset — Track record of cleaning messy datasets, identifying data issues, and implementing validation checks
Problem-solving ability — Can navigate ambiguous situations, diagnose root causes, and develop practical solutions
Clear communicator — Able to explain technical concepts to non-technical audiences and document processes clearly
Basic scripting knowledge — Familiarity with Python, R, or similar tools for data manipulation is a plus but not required
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