Data Analyst
Atlanta, GA · Hybrid
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Axial Search builds long-term talent networks for AI, data, and transformation leaders across the United States, and applying for this role indicates your interest in positions like this one as your next move. This particular position isn't tied to a specific client today, but we actively place people with your background — apply and we'll be in touch when a matching role opens with one of our clients. In the meantime, please make use of our free tools to help with your job search, including our live job market dashboard with salary, skills, and hiring-trend data from thousands of AI transformation roles.
Job market data
Data analyst roles sit at the entry and early-career end of our dataset, but with real upside — the strongest analyst hires we see move into analytics engineering or data science within two to three years. In the Atlanta financial services market specifically, hiring spans regional banks, fintech startups, and healthcare-adjacent finance, with base compensation generally in an $85K–$130K range. Excel, SQL, and a modern BI tool (Power BI, Tableau, or Looker) remain table stakes; the analysts who stand out are the ones who automate their own work and keep asking better questions.
Key responsibilities
Partner with business teams to turn data into decisions via dashboards, deep dives, and ad-hoc analysis
Build and maintain critical reports and self-serve BI assets in Power BI, Tableau, or Looker
Write SQL against the data warehouse and work closely with data engineering on definitions
Lead A/B test readouts and funnel analyses for business partners
Document metrics definitions and evangelize good data practice across the team
Continuously look for opportunities to automate, standardize, and scale analytics work
Dig into unexpected patterns in the data rather than just shipping the dashboard someone asked for
Contribute to the analytics engineering practice as your technical skills grow
Candidate requirements
2+ years of analytics experience with strong SQL fundamentals
Hands-on experience with at least one major BI tool (Power BI, Tableau, or Looker)
Comfortable in a cloud data warehouse (Snowflake, BigQuery, or Redshift)
Strong statistical foundation — able to read and run A/B tests correctly
Excellent written and verbal communication with non-technical stakeholders
Excel and PowerPoint fluency, with the ability to tell a clear story with data
