ANALYTICAL COMPETENCY
EVALUATION
MANAGER OBSERVATIONS & EVIDENCE
Provides accurate insights to business leaders. Is currently working on
refining his "data storytelling" to ensure complex methodologies are
easily understood by non-technical audiences.
Data Governance &
Quality
EXCEEDS
Implemented automated dbt (data build tool) tests to flag nulls and
duplicate records, proactively identifying pipeline breakages before
they impact downstream reporting.
3. KEY ACHIEVEMENTS & QUANTITATIVE IMPACT
Query Optimization & Cloud Cost Reduction:
Refactored 15 legacy ETL SQL scripts that were historically
causing warehouse bottlenecks. This optimization reduced Snowflake compute costs by $45,000 annually and
decreased daily pipeline execution time by 65%.
Executive Dashboard Deployment:
Consolidated 8 fragmented regional sales reports into a single, dynamic
Tableau dashboard. This unified view provided the executive steering committee with real-time visibility into daily
revenue metrics and cohort retention.
A/B Testing Framework for Growth:
Developed a standardized statistical framework for the product growth
team’s A/B tests. By establishing strict minimum sample sizes and controlling for variance, his analysis
confidently validated a checkout flow change that resulted in a 12% lift in conversion rates.
Customer Churn Predictive Analysis:
Performed exploratory data analysis (EDA) and feature engineering to
identify leading indicators of customer churn. His findings directly informed a targeted marketing retention
campaign that saved an estimated $1.2M in recurring revenue.
4. PERFORMANCE STRENGTHS
Samuel’s most prominent strength is his absolute dedication to data integrity. He does not take data at face value;
his first step in any project is rigorous exploratory data analysis and profiling to ensure the underlying dataset is
trustworthy. His code is exceptionally clean, well-commented, and version-controlled via Git, setting a high technical
standard for the rest of the analytics team. Furthermore, his deep understanding of relational database architecture
allows him to query massive datasets with extraordinary efficiency.
5. CONSTRUCTIVE FEEDBACK & AREAS FOR GROWTH
While Samuel’s technical execution is flawless, preparing for a Lead Data Analyst role requires a shift in how he
presents data to the business:
Data Storytelling & Executive Communication:
Samuel tends to focus heavily on the methodology—explaining
standard deviations, variances, and query structures—when presenting to business stakeholders. He needs to pivot
toward a "bottom-line first" approach. Executives need the core insight and the recommended business action
immediately, with the statistical methodology reserved for the appendix.
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Confidential - Manager Evaluation Record
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