Senior Product Analyst

Description Daylight is looking for a skilled, insights driven Senior Product Analyst to join our team and help shape how our product and services evolve. In this role, you will lead data initiatives that directly influence product strategy, operational performance, and customer outcomes. You’ll work across product usage analytics, performance measurement, and business visibility, owning dashboards and KPIs that reflect product health and operational execution. You’ll uncover trends, define metrics, and translate complex datasets into actionable insights that help teams make better decisions, faster. This is a highly cross functional role: you’ll partner closely with Product, Engineering, Operations, and Leadership to ensure Daylight operates with strong data integrity, transparency, and measurable impact. What You’ll Do Analyze product and operational data to identify trends, inefficiencies, and opportunities. Own key analytics projects (ongoing + ad-hoc) aligned with Daylight’s strategic direction. Build and maintain KPIs, dashboards, and reporting for product performance and operational health. Serve as the company’s go-to data partner, helping teams across Daylight extract value from our data assets and make data-informed decisions. Ensure strong data quality, integrity, and transparency, including consistent definitions, metric governance, and trusted reporting infrastructure. Communicate insights clearly through storytelling, visualizations, and concise recommendations for action. Requirements 3–5+ years experience as a product/data/BI analyst (preferably SaaS). Strong SQL and experience working with large, complex datasets. Hands-on experience with BI / visualization tools. Proven ability to translate data into product and business impact. Strong ownership mindset: proactive, detail-oriented, and committed to measurable impact. Strong communication skills - able to translate complex findings into simple, actionable insights for stakeholders. Nice-to-Have Cybersecurity domain experience. Familiarity with cloud data stacks (AWS / BigQuery). Understanding of data pipelines / ETL; exposure to AI/ML-driven analytics.