Manager II, Data Analytics
New
T
The ZebraInsurance technology
Our employees work remotely across the US.Full-TimeManager
Salary155,000 - 185,000 USD per year
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Job Details
- Experience
- 6+ years of experience in a data role (analytics, analytics engineering, or data science), including 3+ years directly managing a team
- Required Skills
- PythonSQLSnowflakeTableauData modelingdbtLooker
Requirements
- Have 6+ years of experience in analytics, analytics engineering, data science, or another data role.
- Have 3+ years of direct team management experience.
- Demonstrate strong proficiency with SQL and a working command of Python.
- Understand data modeling, ETL/ELT pipelines, experimentation, reporting, and statistical analysis.
- Have experience with modern data tools such as Snowflake, dbt, Looker, or Tableau.
- Be fluent with AI tools and how to use them to drive outcomes for an analytics team.
- Have a track record of developing analysts technically and professionally.
- Have experience partnering with business stakeholders to set priorities, including pushing back on requests that will not change a decision.
- Be organized and experienced in creating and optimizing analytics processes.
- Be able to communicate effectively with technical and non-technical stakeholders.
- Have experience managing multiple priorities and adapting in a fast-paced, agile environment.
Responsibilities
- Lead, mentor, and grow a team of product, marketing, and data analysts through technical guidance, career development, and performance coaching.
- Shape the analytics roadmap with the Senior Director of Data and business partners, prioritize work, track progress, remove blockers, and ensure on-time delivery.
- Partner with Product, Marketing, Finance, and Data Engineering to turn business needs into actionable insights and scalable analytics solutions.
- Support new products and initiatives with measurement, instrumentation, analysis, and reporting.
- Drive the quality, reliability, timeliness, and business impact of the team's analysis, insights, data models, and reporting.
- Provide technical oversight on analytics modeling, experimentation, tooling, and coding practices through reviews and retrospectives.
- Expand trusted self-service analytics so teams can answer common questions while analysts focus on higher-value insights and decision support.
- Identify data and measurement gaps and partner with Data Engineering and Engineering to resolve them.
- Help define data strategy, shared metrics, and quality standards across the data lifecycle.
- Help hire, assess, and onboard analytics team members.
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