Bounteous
via Lever
Lead Data Architect
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 1d ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 4d ago
The date the source published, not the day we noticed it (2026-09-10). Last seen at its source just now.
Is it remote?
Marked remote on the employer's board
Their board carries a remote setting on this posting — a field they filled in, not wording we read. The location field names somewhere specific, which is usually where the team or the entity sits.
Who may apply?
United States
The description states no restriction of its own. This is the source's own tag.
Pay not stated
Similar roles pay $195.3k–255k/yr
Middle 50% of 551 listings that do state pay — Engineering · Lead · United States · USD/year. This employer has published no salary; this is what comparable listings we hold disclose, never converted between currencies or periods. How this is calculated.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
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lever employer's own board first seen 4d ago · last seen just now
The listing
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