Principal Data Analyst
Posted Updated
About the role
As a Principal Data Analyst, you’ll turn massive volumes of messy scam, money mule, and internet signals across a multitude of channels into meaningful insights that guide investigations, improve systems and inform product decisions. You will work closely with engineering, product and operations to strengthen how CYBERA understands, measures, and scales its fraud and mule intelligence capabilities. The focus is on transforming complex, high-volume data into clear insights, durable metrics, and decision-ready analytics that support both day-to-day execution and longer-term product strategy.
What you'll do
- Deeply understand complex data sets and the systems that process them
- Explore, clean, and analyze large and messy internet-derived datasets to identify patterns and anomalies
- Build analytics tools that scale analysis and operations
- Build dashboards and reports that make signals understandable to stakeholders
- Translate analytical findings into recommendations for our product, operations, and partner banks
- Collaborate with engineering team to design data-driven features and metrics
- Create outstanding documentation that helps CYBERA scale
- Own the full analytics lifecycle from initial signal to enriched, customer-accessible data
Qualifications
- Experience managing messy internet-derived data including URLs, email and social media sites at scale
- Entrepreneurial mindset energized to overcome novel challenges
- Attention to detail that drives the extraction of signal from challenging datasets
- Strong SQL and data visualization skills (Metabase, Power BI, or similar)
- Experience with Python or R for analysis and automation
- A sharp eye for patterns and outliers in data
- Clear, structured communication. You’ll be the bridge between data and decision-making
It's a plus if you have:
- Experience with data pipelines or data-pipeline tooling (e.g., dbt, Airflow, Dagster)
- Understanding of basic machine-learning workflows or model evaluation
- Familiarity with fraud, risk, or AML data
- Familiarity with cloud data environments (Azure, GCP, or similar)