DATA ANALYST
Self Performance Review
KEY ACCOMPLISHMENTS
Reflecting on the past year, my main priority has been shifting our team from reactive reporting
to proactive, self-serve analytics. One of my most significant wins was automating our weekly
executive sales performance deck. Previously, this required hours of manual CSV exports and
spreadsheet manipulation, which was highly susceptible to human error. I wrote a series of
optimized SQL scripts to extract the data directly from our data warehouse and connected them
to a dynamic dashboard. This initiative not only saved our team approximately ten hours a week
but also provided leadership with real-time access to regional sales trends without needing to
request custom pulls.
Beyond automation, I am particularly proud of the ad-hoc churn analysis I conducted in Q2.
Customer Success noticed a sudden spike in cancellations but couldn't pinpoint the root cause. I
pulled historical usage logs and joined them with support ticket data, discovering that a specific
software bug was disproportionately affecting our enterprise user segment. By quantifying the
exact revenue at risk, I provided the engineering team with the concrete data they needed to
prioritize the patch. This successfully stabilized the churn rate by the end of the quarter and
demonstrated the value of deep-dive exploratory analysis.
CORE COMPETENCIES & STRENGTHS
Data Storytelling:
I have worked hard to ensure I don't just hand over spreadsheets of raw
numbers. I focus on synthesizing complex datasets into clear, actionable narratives for non-
technical stakeholders. By proactively highlighting the "so what?" in my analyses, I help guide
actual business decisions rather than just fulfilling basic data requests.
Technical Rigor & Integrity:
I consistently validate my queries and cross-reference data sources
to ensure high data integrity. I am meticulous about writing clean, well-commented SQL code,
