FREE ACCESS
5,000–10,000 jobs/day
See all jobs on Scoutfield
Search thousands of fresh jobs every day.
Discover
- Fresh listings
- Fast filters
- No subscription required
Create a free account and start exploring right away.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in leading data operations teams, designing quality assurance methodologies, and implementing scalable processes for data management. Proficient in SQL and Python, with a strong understanding of client data usage and experience in collaborating with US-based teams.
Highest-signal resume keywords
Data Operations LeadershipQuality Assurance Methodology DesignSQL ProficiencyLLM and ML Model EvaluationTeam Building and Development
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Data AnalysisData QualityQuality MetricsRoot-Cause AnalysisSampling MethodsTest SetsPrompt DesignModel ComparisonDebugging Data IssuesData Definitions
Soft Skills
CoachingDelegationClient UnderstandingPerformance ManagementCollaboration
Tools & Technologies
SQLPythonBigQueryAirflowLLM Agents
Industry Keywords
CPG DataRetail DataProduct Catalog DataData OperationsData Delivery
Tech Stack
Tools & technologiesAirflowBigQueryPythonSQL
About the role
Key responsibilities & impact- Lead, coach and grow the Data Operations team
- Own priorities, task assignment, performance, hiring and employee growth paths
- Own the quality of every client delivery, including standards, quality checkpoints, acceptance thresholds, escalation paths, peer review and sign-off
- Track quality metrics per delivery and act on trends
- Define and maintain the team’s QA methodology and playbook, including sampling methods, precision and completeness metrics, test sets, root-cause analysis and standardized data guidelines
- Design and scale operational processes for new products and data domains, from input validation and data definitions through quality checks, sign-off and ongoing monitoring
- Work with Delivery, R&D and Product teams on timelines, SLAs, tooling and data needs
- Partner with US-based clients, Customer Success and GTM teams to understand use cases, scope requirements, answer data questions and support sales with samples, feasibility checks and data deep-dives
- Evaluate LLM and ML outputs at scale and guide prompt design, model choice and cost-versus-quality trade-offs
- Introduce automated QA checks, LLM-as-judge evaluation and agents for repetitive data tasks
- Audit samples and investigate issues using SQL and Python
- Set standards for data produced through AI models, rule-based logic or manual review
- Build a self-sufficient team that runs deliveries end to end with minimal R&D support
Requirements
What you’ll need- 5+ years in data roles (data analysis, data quality, data operations or similar)
- Proven experience designing QA methods: sampling, metrics, test sets, acceptance criteria and root-cause analysis
- 4+ years leading a data, analytics or data operations team
- Experience building a team and processes that scaled
- Ability to delegate ownership and develop people
- Understanding of how clients use data to make decisions and ability to review data from the client's point of view
- Native-level or full professional proficiency in English
- Experience working directly with US clients or US-based teams
- Hands-on work with LLMs and ML models, including prompt design, model comparison and evaluating output quality
- Strong SQL skills
- Comfortable debugging data issues and reading pipeline configuration
- Experience with CPG, retail or product catalog data (UPCs, taxonomies, item masters) is an advantage
- Background in Python, BigQuery and Airflow is an advantage
- Experience building LLM agents or automation for data and QA work is an advantage
- Experience in a fast-growing startup is an advantage
Benefits
Comp & perks- Full-time employment
- Remote/hybrid work arrangement