Lead Data Architect

Bounteous is a global AI Services firm where agentic engineering and human experience converge to deliver transformative business outcomes for the enterprise. We help organizations design, build, and scale AI-driven products, platforms, and processes. With more than 5,000 team members worldwide, Bounteous delivers AI that sticks, powering adoption and outcomes that move organizations from experimentation to true transformation. Bounteous is backed by New Mountain Capital, a New York-based growth-oriented investment firm that emphasizes business building.

About the Role This is a data-heavy, hands-on leadership role for someone who can dig deep into complex datasets themselves and guide a team through equally complex problems. We are looking for someone who can only reviews others' work but also is a strong individual contributor with senior-level technical caliber who can roll up their sleeves on the hardest analyses, set the technical bar for the team, and unblock analysts when the work gets genuinely difficult.

Key Responsibilities • Lead and execute complex, high-stakes analyses — deep-dive investigations, statistical modelling, and multi-source data problems that require real technical depth, not just query-writing • Guide and unblock the team on difficult technical problems: query optimization, data modeling challenges, messy/ambiguous datasets, and edge cases junior analysts get stuck on • Write advanced, performant SQL against large and complex datasets (multi-table joins, window functions, query optimization, working with messy or poorly documented schemas) • Build and maintain robust data models and pipelines in partnership with Data Engineering, and know enough about the underlying infrastructure to reason about data quality issues at the source • Apply statistical methods (experimentation/A-B testing, regression, cohort and trend analysis) to move beyond surface-level reporting into rigorous, defensible conclusions • Design and build dashboards and reporting frameworks, but also know when a dashboard isn't enough and a deeper custom analysis is needed • Set and enforce technical standards for the team — code review, data quality checks, analytical rigor, and documentation • Mentor analysts by working alongside them on hard problems, not just reviewing finished output • Translate ambiguous, loosely-defined business questions into structured, technically sound analytical approaches • Present complex findings to senior stakeholders in a way that's rigorous but accessible • Own the technical roadmap for the analytics function's tools, data models, and processes

What We're Looking For Required: • 9+ years of hands-on data analysis experience, with demonstrated depth (not just breadth) in complex, high-volume datasets • 2+ years leading or mentoring analysts on technically difficult work, not just project managing • Expert-level SQL: comfortable with window functions, query optimization, and untangling large/messy schemas without much documentation • Experience with cloud data warehouse/ lakehouse platforms such as Snowflake and Databricks; ability to work across both during platform transitions is a plus. • Solid grounding in statistics — experimentation design, hypothesis testing, regression, and knowing when correlation isn't enough • Experience with a BI/visualization tool (e.g., Tableau, Looker, Power BI), used as one tool among several rather than the primary skill set • Demonstrated ability to independently solve ambiguous, multi-layered data problems end to end • Proven ability to communicate complex technical findings clearly to senior, non-technical stakeholders

Good to Have • Direct experience designing and analyzing A/B tests or experimentation frameworks • Exposure to data engineering concepts (schema design, ETL, data warehousing) sufficient to diagnose upstream issues • Experience formally managing analysts (performance, growth plans), not just technical mentorship

Skills & Attributes • Deep, practical fluency with data — someone who remains close to the analysis itself rather than operating purely at a supervisory level • Strong analytical and statistical judgment, with the ability to identify data quality issues and edge cases that less experienced analysts may overlook • Technically credible with data engineering counterparts while remaining an effective communicator to business stakeholders • Builds credibility with the team through the quality and rigor of their own work, in addition to formal mentorship