Note: The job is a remote job and is open to candidates in USA. Bounteous is a global AI Services firm that delivers AI-driven products, platforms, and processes for enterprise transformation. The Lead Data Architect will lead complex data analyses, guide analysts through difficult technical problems, establish analytical and data quality standards, and oversee the technical roadmap for analytics tools, models, and processes.
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
- Promote and enforce awareness of key information security practices, including acceptable use of information assets, malware protection, and password security protocols
- Identify, assess, and report security risks, focusing on how these risks impact the confidentiality, integrity, and availability of information assets
- Understand and evaluate how data is stored, processed, or transmitted, ensuring compliance with data privacy and protection standards (GDPR, CCPA, etc.)
- Ensure data protection measures are integrated throughout the information lifecycle to safeguard sensitive information
Skills
- 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
- 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
- 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
Benefits
- Remote work arrangement
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