About the Role
You'll work closely with Product, Operations, Payment, Finance, Risk, and Data Engineering to transform data into actionable insights and drive data-informed decision-making across the organization. You should be analytical, business-oriented, and thrive in a fast-paced environment, able to quickly understand complex business domains, collaborate effectively with cross-functional teams, and independently deliver high-quality analytical solutions with minimal onboarding.
Requirements
- Bachelor's degree in a quantitative field.
- 3+ years of experience in BI, Data Analytics, or similar roles.
- Advanced SQL skills (joins, window functions, CTEs, optimization).
- Hands-on experience with Tableau, Power BI, or equivalent.
- Experience working closely with Data Engineering teams.
- Strong understanding of data warehouse concepts and metric design.
- Excellent analytical, communication, and stakeholder management skills.
- Ability to quickly learn new business domains with minimal onboarding.
- Self-motivated and comfortable in a fast-paced environment.
- Experience in cryptocurrency, digital assets, fintech, payments, or exchanges preferred.
- Experience with Databricks, Spark SQL, Snowflake, BigQuery, or Redshift preferred.
- Python (Pandas) for analysis and automation preferred.
- Experience designing KPI frameworks and executive dashboards preferred.
- Knowledge of experimentation, data governance, and metadata management preferred.
Responsibilities
- Partner with business stakeholders to understand analytical requirements and translate them into scalable reporting and dashboard solutions.
- Design, develop, and maintain dashboards and self-service reporting using Tableau or other BI tools.
- Write efficient and optimized SQL to extract, transform, and analyze large-scale datasets.
- Work closely with Data Engineering teams on data modeling, ETL pipelines, metric implementation, and data quality improvements.
- Define, maintain, and govern business KPIs and metric definitions to ensure consistency across teams.
- Perform ad hoc analyses to identify business opportunities, operational issues, and performance drivers.
- Monitor key business metrics, investigate anomalies, and provide timely insights and recommendations.
- Support business initiatives through performance analysis, experimentation, and strategic reporting.
- Present analytical findings clearly to both technical and non-technical stakeholders.
- Continuously improve reporting automation, analytical processes, and data governance.