CompanyCam

Product Manager

• Lead and mentor a team of 2 Product Managers
• Lead discovery efforts ensuring value for customers
• Build and maintain relationships across teams
• Drive growth through end-to-end initiatives
• Launch new features and define success metrics

Group Product Manager

Customer Success Manager

• Lead, coach, and develop a team of Mid-Market Customer Success Managers and Implementation Specialists, setting a high bar for customer outcomes and team performance
• Create a culture of accountability, ownership, and continuous improvement across the customer success organization
• Manage team performance across onboarding, adoption, retention, renewals, and expansion initiatives
• Own team-level KPIs including GRR, NRR, customer health, implementation timelines, and retention metrics
• Partner with team members on account strategy, customer escalations, renewal planning, and risk mitigation efforts
• Improve onboarding workflows and implementation processes to help customers achieve value quickly and consistently
• Use customer data and trends to identify risks, opportunities, and areas for operational improvement
• Collaborate cross-functionally with Sales, Product, Engineering, and leadership to improve the customer experience and influence segment strategy
• Support strategic customer relationships and high-impact accounts when needed
• Assist with hiring, onboarding, and ongoing development of Customer Success team members

Manager, Customer Success – Mid-Market

Data Engineer

• Design and maintain event-driven pipelines from backend systems (Ruby on Rails) to Snowflake.
• Own event pipelines from backend app through Snowplow ingestion.
• Establish event tracking standards, documentation, and training for engineering teams.
• Support feature flagging and A/B testing infrastructure requirements.
• Optimize existing data workflows for efficiency and performance.
• Manage Snowflake administration, performance optimization, and cost management alongside the data team.
• Build reverse ETL processes and marketing automation data flows using tools like Census.
• Collaborate to optimize Snowplow Analytics for event tracking and schema management.
• Partner with analytics engineers on dbt deployment, testing, and production workflows.
• Act as the technical bridge between engineering and analytics for pipeline questions.
• Provide technical guidance to product and engineering teams on event implementation.
• Lead training sessions and office hours on event tracking and troubleshooting.
• Participate in architecture discussions and cross-team technical decision-making.

Senior Data Engineer

Security Engineer

• Administer CompanyCam's Vanta Professional instance
• Own NIST CSF 2.0 and SOC 2 Type II framework alignment
• Identify and route compliance gaps
• Maintain evidence libraries and audit trails
• Prepare risk reporting for the Enterprise Risk Committee
• Own the risk register
• Conduct vendor security assessments
• Own the intake and response process for security questionnaires
• Support security awareness initiatives

Security & Compliance Analyst

Data Scientist

• Lead and develop a team of Product Data Analysts and an embedded Data Scientist through coaching, prioritization, and hands-on analytical leadership
• Establish clear expectations and quality standards for product analytics, experimentation, and data science work
• Build scalable operating processes for intake, prioritization, stakeholder communication, and cross-functional collaboration
• Partner with Product and Engineering leaders to support data-informed product strategy and decision-making
• Help teams connect product behavior to activation, adoption, engagement, retention, and customer value
• Guide experimentation strategy and ensure rigorous A/B testing methodologies and actionable interpretation of results
• Improve product metrics and scorecards that measure product health, workflow usage, launch performance, and customer outcomes
• Enable thoughtful self-service analytics through tools such as Metabase, Amplitude, Secoda, and dbt
• Partner cross-functionally with Analytics Engineering, Data Engineering, Engineering, and ML/AI teams to improve ownership clarity and data quality
• Translate complex analysis and data science work into clear, decision-oriented insights for technical and non-technical stakeholders
• Identify practical, high-impact opportunities for predictive modeling, classification, recommendation systems, and AI-enabled product intelligence
• Help stakeholders distinguish between meaningful data science opportunities and lower-value or overly speculative work
• Contribute directly to high-priority analyses, ambiguous product questions, and strategic initiatives when needed
• Protect the team from low-leverage work while creating space for higher-impact analytical and strategic contributions

Manager, Product Analytics – Data Science

Full-stack Engineer

• Build new features and enhancements on top of established integrations based on customer needs and data
• Maintain and upgrade existing integrations, including endpoint updates and deprecation management
• Investigate and debug issues using logs, monitoring tools, and a whole lot of curiosity
• Use AI tooling to move faster and work smarter across debugging and development workflows
• Partner closely with your team lead to keep maintenance and enhancement work in its own lane, so new integration builds stay on track

Software Engineer II

Data Analyst

• Partner with Product Managers to define KPIs, measure success, and support continuous discovery practices
• Analyze product usage, customer behavior, and funnel performance to identify trends and opportunities
• Support experimentation and A/B testing efforts by measuring results and translating findings into actionable recommendations
• Build and maintain dashboards and reporting in tools such as Metabase and Amplitude
• Develop analyses that help teams improve product adoption, engagement, and retention
• Work with cross-functional teams to ensure consistent metric definitions and trusted reporting
• Leverage SQL, dbt, and analytics tools to transform and analyze product data
• Enable self-service analytics by training stakeholders on dashboards, reporting tools, and best practices
• Communicate complex findings clearly and effectively to both technical and non-technical audiences
• Help foster a culture of experimentation, curiosity, and data-driven decision-making across Product and Engineering teams