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This isn't a traditional 'pull reports and make charts' analyst role. As an entry level Data Analyst, you’ll operate at the intersection of data engineering, technical support, and operations. You’ll help troubleshoot data issues, support pipeline reliability, and fulfill ad-hoc data requests while gaining hands-on exposure to the full data ecosystem. This is a fast-paced, high-impact role where adaptability, curiosity, and problem-solving are key. You don’t need to know everything on day one—we’re looking for someone proactive, resourceful, and eager to learn. The ideal candidate takes initiative, explores problems independently, and uses available tools (including AI) to find solutions efficiently.
Job Responsibility:
Data Issue Triage: Investigate and resolve L2/L3 data-related issues by analyzing datasets, writing queries, and clearly documenting findings for internal teams
Pipeline Monitoring & Support: Develop an understanding of data pipelines, identify potential points of failure, and proactively flag issues
Data Quality & Validation: Perform quality checks on datasets (both spatial and tabular) to ensure accuracy before distribution
Data Integration: Assist with onboarding new data sources into ETL processes, including setup, testing, and validation
AI-Driven Problem Solving: Leverage AI tools to accelerate troubleshooting, learn new systems, and improve efficiency—while applying sound judgment
Requirements:
SQL: Comfortable writing queries using joins, filters, and aggregations to explore and analyze data
Programming Exposure: Familiarity with Python, R, or similar scripting languages
Ticketing Systems: Experience with tools like Jira, Asana, or similar for managing and tracking work
Version Control: Basic understanding of Git (commits, branches, pull requests)
0–2 years of experience in data analytics, technical support, or a related field
Naturally curious and resourceful
able to research, test, and problem-solve independently
Strong attention to detail with a focus on data accuracy
Comfortable working in a dynamic environment with shifting priorities
Clear and concise communicator, especially in written formats (tickets, documentation, messaging tools)
Interested in growing toward a data engineering career path
Nice to have:
Experience with spatial data (GeoJSON, Shapefiles) or GIS tools (QGIS, ArcGIS)
Exposure to ETL processes, data warehousing, or pipeline tools (e.g., Airflow, dbt)
Familiarity with BI tools such as Tableau, Looker, or Power BI
Background in SaaS, marketing analytics, tourism, or agency environments
Experience using AI tools in day-to-day workflows and the ability to articulate how they improve productivity
What we offer:
medical, vision, dental, and life and disability insurance