Lead Data Analyst

Bachelor's degree in Computer Science, Data Science, related field, or equivalent experience. 7+ years in data analysis, business intelligence, or related analytical roles. 1+ years in senior-level roles with leadership and cross-functional responsibilities. Experience working with modern data stacks and cloud data warehouses (BigQuery, Snowflake, Redshift, or similar). Expert in SQL and data analysis. Strong proficiency in Python for statistical analysis. Advanced experience with Looker or similar BI tools. Knowledge of statistical methods, A/B testing, experimentation design, and causal inference. Experience with data modeling concepts and collaborating with analytics engineers on data infrastructure. Familiarity with predictive modeling, machine learning concepts, or advanced analytics is a plus. Proven ability to lead analytical initiatives and mentor team members. Strong problem-solving skills. Exceptional communication skills translating complex analyses into compelling narratives. Comfortable navigating ambiguity and driving decisions in fast-paced environments. Experience influencing senior stakeholders and driving business strategy through data insights.

Lead complex, cross-functional analyses informing strategic business decisions and product direction. Own end-to-end analytical projects from problem definition to impact measurement. Design and implement analytical frameworks, metrics, and dashboards for self-service insights. Define and enforce standards for analytical rigor, documentation, and best practices. Translate ambiguous business questions into structured analytical approaches. Lead high-complexity initiatives spanning multiple teams and business domains. Set project direction, ensure timely delivery, and maintain high-quality analytical standards. Translate complex analytical findings into clear, actionable recommendations. Mentor data analysts on analytical techniques, business acumen, and career growth. Partner with leadership to identify opportunities where data can drive efficiency, growth, and business impact. Facilitate alignment across stakeholders by integrating analytical insights with business context. Champion a culture of experimentation, measurement, and data-driven decision-making. Foster knowledge sharing, continuous feedback, and cross-team collaboration.