Why This Job is Featured on The SaaS Jobs
Product Operations roles sit at a key junction in many SaaS businesses: the point where product data, configuration, and customer-facing experiences have to stay consistent at scale. This listing is notably anchored in catalog and taxonomy accuracy, change management, and account-level configuration, indicating a SaaS environment where operational precision directly affects product usability and downstream customer outcomes.
For SaaS career development, the work builds durable skills in operating alongside Product and Engineering without being purely delivery or support. The emphasis on root-cause analysis, workflow bottleneck removal, and measurable operational metrics mirrors how mature SaaS teams professionalise internal systems over time. Exposure to SQL-driven investigation, large datasets, and automation playbooks also translates well across SaaS domains that rely on structured data and repeatable processes.
This role tends to suit an operator-analyst who prefers structured problem solving over open-ended ideation, and who enjoys turning recurring requests into clearer rules and scalable routines. It will fit someone comfortable coordinating across multiple functions and maintaining high data standards, particularly at a stage where hands-on execution and process improvement are equally important.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
Key Responsibilities
Own day-to-day product operations activities including change management requests, product maintenance, product tagging, banner tagging, PIM matching, and configuration updates across customer accounts.
Ensure high levels of data quality and operational excellence by validating product classifications, taxonomy mappings, catalog configurations, and onboarding-related deliverables.
Investigate operational issues, identify root causes, and work cross- functionally with Product, Engineering, Data Science, and Customer Success teams to drive timely resolution and long-term fixes.
Analyze operational workflows, identify bottlenecks and inefficiencies, and recommend process improvements that improve accuracy, scalability, and turnaround times.
Drive automation and AI-assisted workflow initiatives by helping translate manual processes into scalable systems, business rules, and operational playbooks.
Support strategic and operational initiatives across the organization, taking ownership of new programs, process rollouts, and continuous improvement efforts as business needs evolve.
Required Qualifications
3–5 years of experience in Operations, Product Operations, Data Operations, Business Operations, Catalog Operations, or Analytics
Strong analytical and problem-solving abilities Hands-on SQL experience Advanced Excel / Google Sheets skills
Experience working with large datasets and operational workflows
Strong attention to detail and data quality
Excellent written and verbal communication skills
Preferred Qualifications
Experience in e-commerce, retail technology, marketplaces, catalog operations, or product data management
Experience with taxonomy management, product classification, or metadata operations
Familiarity with Jira, Confluence, BigQuery, Databricks, or similar platforms
Exposure to automation tools, Python, scripting, or workflow automation platforms
Familiarity with AI-assisted workflows and Generative AI tools
Success Metrics
SLA adherence for change requests
Product Maintenance activities accuracy and throughput
Reduction in recurring operational issues
Automation opportunities identified and implemented
improvements in operational efficiency and data quality
Ideal Candidate
A highly analytical operator who enjoys solving messy operational problems, investigating root causes, improving data quality, and building scalable processes. The ideal candidate combines operational discipline with curiosity about automation and AI, and is motivated to eliminate repetitive work through smarter systems.
