The opportunity
We are looking for a Product Analyst to join our Data team at Bridge.
What you'll do
Payments quality: transaction speed, uptime/reliability, error rates, and support ticket trends across our payment rails and products.
Product experience: how customers interact with Bridge's dashboard, onboarding flows, and other surfaces, and where friction is costing us adoption or trust.
Own the data needs for your focal area end-to-end: define and refine the right metrics, build the pipelines to source them, and drive them into decisions — without needing significant hand-holding.
Analyze payments quality signals (latency, uptime, failure/error rates,: support ticket volume and themes) to identify where customers are experiencing friction and quantify the business impact.
Analyze product and UX data (dashboard usage, onboarding funnels, feature: adoption) to find opportunities to improve the customer journey.
Design, build, and maintain robust, well-documented data pipelines and ETL: processes that make customer experience data easy to access, trust, and query across the company.
What they're looking for
- + years of experience as a data analyst or in a similar analytics role,: ideally at a payments, fintech, or high-growth technology company.
- Excellent SQL skills, with experience querying and modeling data in a modern: warehouse/lakehouse environment (e.g., Databricks).
- Hands-on experience building and maintaining data pipelines and ETL: processes, and comfort working with version control (GitHub) as part of a technical workflow.
- Strong analytical and diagnostic skills: able to independently investigate a problem, identify root cause, and turn findings into a clear recommendation.
- Experience building dashboards and visualizations that communicate insights: clearly to both technical and non-technical stakeholders.
- Demonstrated ability to work independently, take ownership of a focal area,: and manage multiple concurrent priorities with minimal oversight.
- Strong written and verbal communication skills, with the ability to explain: technical or data-driven findings to non-technical audiences.
- Experience analyzing payments data specifically (transaction quality,: uptime/reliability, support ticket trends) or product/UX data (onboarding, dashboard engagement, funnels).
