Senior/Staff Data Analyst
New
Continental U.S.Full-TimeStaff
Salary180,000 - 210,000 USD per year
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Job Details
- Experience
- 7+ years of experience
- Required Skills
- PythonSQLArtificial IntelligenceData AnalysisSnowflakeData engineeringdbt
Requirements
- 7+ years of experience as a data analyst, analytics engineer, or data scientist.
- Built analytics practices at a startup or early-stage company.
- Deep fluency in SQL.
- Comfortable reading and writing Python.
- 1+ years of hands-on experience with dbt, Snowflake, or equivalent in real-world production environments.
- Proven ability to own full-stack analytics projects end-to-end: data engineering, analysis, dashboarding, and stakeholder reporting.
- Operated at the Senior or Staff level.
- Strong analytical instincts — pull own data, form hypotheses, let evidence shape decisions.
- Clear, precise communicator — translate complex findings to technical and non-technical audiences.
- Comfortable with ambiguity and pace.
- Strong project management skills.
- Low ego, high ownership.
Responsibilities
- Define how Future measures what matters — from product health to business performance to coaching outcomes.
- Build relationships with data consumers across the organization to develop a clear point of view on what our data products should do and how they should be designed.
- Own the analytics roadmap. Decide what to build, what to instrument, and what to prioritize.
- Develop and maintain a strong perspective on how data and AI should power decision-making as we scale.
- Design and implement data transformation pipelines in dbt to create clean, reliable, and interpretable datasets.
- Partner with the broader team to integrate and normalize data from 30+ disparate sources into a flexible, well-documented data model.
- Perform strategic analyses on large, complex datasets and distill findings into clear, actionable recommendations for leadership.
- Analyze coaching outcomes, member engagement, and retention data to surface what drives lasting behavior change.
- Build dashboards, reports, and visualizations that surface the metrics that matter and help stakeholders make better decisions faster.
- Use AI to accelerate data exploration, synthesis, and documentation, and bring a perspective on how AI should be woven into our analytics practice itself.
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