Lead Data Analyst
New
J
JobgetherData Analytics
Based in the United StatesFull-TimeLead
Salary$160,000–$200,000, depending on experience and level.
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Job Details
- Experience
- 8+ years of experience in data analysis, business intelligence, or a comparable analytical discipline
- Required Skills
- PythonSQLBusiness IntelligenceSnowflakeData modelingBigQueryRedshiftLooker
Requirements
- 8+ years of experience in data analysis, business intelligence, or a comparable analytical discipline.
- At least 2 years operating at a senior analyst level.
- Expert-level SQL skills.
- Strong Python capabilities for statistical analysis and data exploration.
- Advanced experience with Looker or a comparable business intelligence platform.
- Hands-on experience with modern cloud data warehouses such as BigQuery, Snowflake, or Redshift.
- Working knowledge of experimentation design and causal inference.
- Experience partnering with analytics engineers on data modeling and warehouse architecture.
- Demonstrated ability to mentor and influence colleagues without formal authority.
- Bachelor's degree in a quantitative or technical discipline, or equivalent professional experience.
Responsibilities
- Lead end-to-end analyses for complex and ambiguous business questions, from problem definition and data exploration through recommendations, implementation, and impact measurement.
- Design dashboards, reporting frameworks, and analytical tools that enable stakeholders to answer recurring questions independently.
- Establish high standards for analytical rigor, methodology, documentation, and communication while reviewing and mentoring the work of other analysts.
- Apply statistical methods, experimentation frameworks, and causal inference techniques when they provide stronger evidence and more reliable conclusions.
- Partner with senior leaders to identify opportunities where data can improve efficiency, growth, decision-making, and business outcomes.
- Collaborate closely with analytics engineers to influence data modeling, warehouse structures, and canonical definitions.
- Transform recurring analytical requirements into durable data models and scalable solutions.
- Translate complex findings into concise, understandable recommendations for both technical and non-technical stakeholders.
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