Senior Product Analyst, Data‑Driven Customer Engagement

Job description

A leading organization is seeking a senior product analyst to support customer engagement and channel innovation initiatives. This individual contributor role focuses on end‑to‑end delivery of data‑driven campaigns, experiments, and technical initiatives that directly impact customer experiences. The position operates at the intersection of business strategy, data, and technology, requiring strong ownership, judgment, and the ability to work independently in a fast‑moving, regulated environment.

The senior product analyst serves as a connector across data engineering, data science, marketing, growth, and business teams. The role emphasizes clear requirements definition, execution readiness, and post‑launch monitoring rather than roadmap ownership or people management. The individual is expected to proactively identify risks, challenge assumptions, and ensure initiatives are production‑ready and well governed.

Responsibilities

• Own end‑to‑end execution of data‑driven campaigns, experiments, and technical initiatives from discovery through launch and post‑launch monitoring.
• Translate campaign and experiment designs into clear business requirements, success metrics, and acceptance criteria that support high‑quality technical delivery.
• Partner closely with data engineering and data science teams to support delivery planning, release readiness, and issue resolution.
• Analyze performance data to identify insights, trends, risks, and optimization opportunities across initiatives.
• Monitor live campaign and experiment health, proactively surfacing anomalies, risks, and improvement opportunities to stakeholders.
• Lead structured discovery, ideation, and alignment sessions that connect business goals, growth levers, and technical constraints.
• Manage cross‑functional stakeholders across business, marketing, legal, and technical teams, navigating competing priorities and resolving misalignment.
• Identify gaps in execution readiness, data quality, or measurement, and raise issues early with clear recommendations.
• Document requirements, decisions, and outcomes to support alignment, continuity, and reduced delivery friction.
• Reinforce consistent operating practices related to intake quality, execution readiness, and communication standards.

Required experience and skills

• Six or more years of experience driving business outcomes across analytics, product development, technical delivery, or related roles supporting data‑driven initiatives.
• Demonstrated experience owning end‑to‑end execution of campaigns, experiments, or technical products in partnership with engineering and data teams.
• Strong data fluency, including the ability to interpret analyses, reason through ambiguous questions, and evaluate inputs from multiple data sources.
• Proven systems thinking across data science and data engineering, with the ability to anticipate downstream business risks related to data quality, logic, and orchestration.
• Experience operating effectively in complex, matrixed environments with both business and technical stakeholders.
• Strong written and verbal communication skills, with the ability to translate complex topics for mixed technical and business audiences.
• Self‑starter mindset with the ability to work independently, take initiative, and deliver results with minimal supervision.

Preferred experience

• Experience supporting customer engagement, lifecycle, CRM, or growth experimentation initiatives.
• Exposure to regulated or high‑consequence environments such as healthcare, financial services, or privacy‑sensitive consumer technology.
• Familiarity with Agile or Scrum‑based delivery models, with a pragmatic and execution‑focused approach to process.

FAQ

1. What does “end-to-end ownership” mean in this role?
End-to-end ownership means taking initiatives from initial discovery through launch and post-launch monitoring. This includes defining requirements, aligning stakeholders, ensuring delivery readiness, and tracking performance after release. The role focuses on execution quality rather than roadmap ownership.

2. How is success measured for campaigns and experiments?
Success is defined through clear, pre-agreed metrics such as conversion rates, engagement lift, retention, or revenue impact. The analyst ensures measurement frameworks are in place before launch and validates results post-deployment. Continuous monitoring helps identify optimization opportunities and risks.

3. What kind of collaboration is expected with data engineering and data science teams?
The role requires close partnership to translate business needs into technical requirements and ensure smooth delivery. This includes aligning on data pipelines, experiment design, and model outputs. The analyst also supports debugging, validation, and release readiness.

4. How technical does this role need to be?
The role requires strong data fluency and systems thinking rather than deep coding expertise. You should be comfortable interpreting analyses, validating logic, and understanding data workflows. Familiarity with data structures, experimentation frameworks, and analytics tools is important.

5. What tools or platforms are commonly used in this role?
Typical tools may include SQL-based data warehouses, analytics platforms (e.g., GA4, Amplitude), experimentation tools, and CRM systems. Documentation and collaboration tools like Jira, Confluence, or similar are also commonly used. Toolsets may vary depending on the organization’s tech stack.

6. How does this role differ from a Product Manager?
This role focuses on execution, delivery readiness, and performance monitoring rather than owning product strategy or roadmap. It emphasizes translating requirements, ensuring quality delivery, and validating outcomes. There is no direct people management responsibility.