Revenue & Operations Analytics Engineer

Cssmerge; Los Angeles (CA); On-site; USD 93,000 - 109,000; Full time

Benefits offered by this job

Medical/Dental/Vision

Life & Disability Insurance

Voluntary Insurance

Pet Insurance

401(k)

Health Savings Account

Flexible Spending Accounts

Discretionary vacation

8 holidays

Paid sick time

Bereavement leave

Parental leave

Job summary

Cssmerge in Los Angeles is seeking an Operations Analytics Engineer to join our data and operations team. In this hybrid role, you’ll monitor the health of our top revenue products through data: identifying anomalies in performance, surfacing issues across data pipelines or operations, and helping ensure that what we deliver to customers is both accurate and actionable.

This is not just a technical data quality role.

Qualifications

• Strong analytical skills and experience with business KPIs and operational metrics.
• Ability to surface issues across data pipelines and operations.
• Proven track record in driving data-informed decision making.

Responsibilities

• Monitor core KPIs across customers, segments, and systems to identify unexpected drops, spikes, or anomalies.
• Investigate root causes of performance issues, whether data issues, operational breakdowns, or product changes.
• Build and maintain dashboards, alerts, and monitors to detect early signs of degradation in revenue recapture outcomes.
• Collaborate with Engineering to debug technical issues, with Operations to flag workflow breakdowns, and with Product to validate system behavior.
• Maintain and evolve business health checks serving as an early-warning system.
• Create documentation and playbooks to improve response times and proactive quality practices.

Skills

SQL

Analytical thinking

Cross-functional collaboration

Ownership bias

Tools

Looker

Tableau

Mode

Job description

Cssmerge in Los Angeles is seeking an Operations Analytics Engineer to join our data and operations team. In this hybrid role, you’ll monitor the health of our top revenue products through data: identifying anomalies in performance, surfacing issues across data pipelines or operations, and helping ensure that what we deliver to customers is both accurate and actionable.

This is not just a technical data quality role.