Business Intelligence Sr Analyst

Texas Capital provides a variety of benefits to colleagues, including health insurance coverage, wellness program, fertility and family building aids, life and disability insurance, retirement savings plans with a generous 401K match, paid leave programs, paid holidays, and paid time off (PTO).

Support the development of analytics solutions for our Finance & Risk pod, with a primary focus on Finance, by translating business problems into analytical products that drive executive decision‑making. You’ll build interactive dashboards and data applications, collaborate closely with our centralized data engineering team to help shape the Finance & Risk analytics roadmap, and work hands‑on in SQL and Python across a modern Snowflake stack (with opportunities to leverage Streamlit, Posit, and emerging agentic AI).

The role offers direct exposure to senior leadership, including the CFO and CRO, and meaningful influence on Finance strategy through analytics. This position is a strong fit for a mid‑level analyst ready to step into product responsibilities, stay hands‑on technically, and deliver measurable business impact.

• Product Ownership
  :
  Lead the development and execution of the Finance & Risk analytics roadmap; contribute to prioritization decisions and help translate requirements into technical specifications with stakeholders and data engineers.
• Interactive Applications
  :
  Develop analytics applications in Streamlit, Posit, and our existing BI platforms; own the full lifecycle from requirements to production deployment and ongoing iteration.
• Last‑Mile Analytics
  :
  Write SQL and Python for business logic, measures, and semantic layer definitions that drive accuracy, consistency, and strong performance.
• Requirements Partnership
  :
  Validate that data engineering output meets business needs; provide actionable feedback on data freshness, accuracy, completeness and overall usability.
• Stakeholder Communication
  :
  Gather requirements from Finance & Risk teams; present findings to executive leadership with a clear articulation of business impact and decision implications.
• Advanced Analytics & Innovation
  :
  Explore and pilot agentic AI use cases for Finance & Risk analytics; identify opportunities where AI can augment human analysis, improve efficiency, or create new business value.

• 5+ years of hands‑on experience in analytics, analytics engineering, BI development, or data science experience (or equivalent practical experience).
• Advanced SQL (e.g., CTEs, window functions, performance tuning) and Python for data manipulation (pandas/Num Py), with clean, version‑controlled code (git workflows).
• Strong understanding of dimensional modeling concepts (e.g., star schemas, conformed dimensions, and SCD handling) and the ability to apply these principles when defining business logic, metrics, and semantic layers (hands‑on model development is helpful but not required).
• Experience with Snowflake or another modern cloud data warehouse
  , with willingness to deepen Snowflake expertise.
• Interactive dashboard development in a modern BI platform (Power BI, Sigma, Looker, or similar), with attention to usability and performance.
• Strong communication and cross‑functional collaboration skills
  , including the ability to translate between business needs and technical implementation.
• Bachelor's degree in Computer Science, Data Science, Statistics, Engineering, or related quantitative field.

• Analytics application development in Streamlit or Posit (or strong Python fundamentals with willingness to learn quickly).
• Familiarity with Finance & Risk domains (e.g., financial reporting/GL, budgeting & forecasting, or risk analytics).
• Exposure to agentic AI or LLM‑powered analytics
  .
• Experience with predictive modeling, time‑series analysis, or statistical techniques
  .
• Comfort with agile development practices
  , code reviews, and iterative product delivery.
• Experience presenting to senior leadership (C‑suite exposure a plus).

Our Data & Analytics organization is built around federated “pods,” each aligned to a major business area and paired with our centralized data engineering…