Data Analyst

Well-known player in the capital markets space. Lean, fast-moving data org that sits at the intersection of on-prem infrastructure and cloud (AWS). High ownership, high visibility - this team fields data requests directly from sales, marketing, and support, and is actively building toward a more automated, self-sufficient data environment across the business. Role Description This is a hands-on data analyst role with real scope to grow. Day-to-day you're translating business questions into SQL queries, building dashboards and reports, and partnering with internal teams to surface insights they can actually use. About 25% of the role is requirements gathering - understanding what the business is actually asking for - and the majority is building. Some requests are one-offs (write a query, deliver it in Excel). Others are repetitive enough to warrant a proper dashboard. Over time the goal is to make business users more self-sufficient - better data access, better data literacy org-wide - which means this role evolves. This morphs into data engineering territory as the platform matures. AI tooling (they're heavy Claude users) and NLP are part of the roadmap. Fast-paced, results-driven culture. Strong promotion track for people who deliver., font-family: inherit; font-size: inherit;">Location: Hybrid - NYC (3 days/week, typically M/T/W or T/W/Th)n

• Strong SQL - must be able to write queries confidently (joins, CTEs, window functions will come up in the interview)
• Experience working with relational databases (Oracle, SQL Server, Postgres - doesn't matter which)
• 2+ years of experience minimum
• Bachelor's degree
• Problem-solver mentality - they'll teach the tools, they can't teach the mindset

Nice to Have

• AWS experience (QuickSight or similar - strong plus)
• BI/reporting tool experience (Jaspersoft, QuickSight, Tableau, Power BI - if you've used one you'll pick up the others)
• Financial services, compliance, or capital markets background
• Familiarity with AI tools in a work context, a proper dashboard. Over time the goal is to make business users more self-sufficient - better data access, better data literacy org-wide - which means this role evolves. This morphs into data engineering territory as the platform matures. AI tooling (they're heavy Claude users) and NLP are part of the roadmap.