Sales Engineering Operations Analyst - Salesforce, Reporting & AI

About the role

The Sales Engineering Operations Analyst owns the end to end health of Sales Engineering workflows by coordinating data, dashboards, and tool administration across the organization. This role translates complex operational realities into clear metrics that keep Sales Engineering consistently productive and visible to stakeholders. You will partner closely with reporting and Salesforce teams to define, test, and execute operational improvements that scale. By maintaining Salesforce and supporting tools, you ensure the Sales Engineering engine runs smoothly and decisions are based on reliable information. You will validate reporting accuracy and usability so leadership can trust the insights used for forecasting and planning. A core part of the role is extending AI-enabled workflows that boost efficiency and insight generation for Sales Engineering. You will sustain and improve existing SFDC and Vivun processes so the team can focus on selling and supporting customers.

Key facts

What you'll do

Maintain Sales Operations Power BI dashboards that deliver clear visibility and decision support for Sales Engineering stakeholders.

Validate reporting accuracy and usability to satisfy leadership requirements and operational objectives for Sales Engineering.

Support Salesforce and the Vivun toolset to enable smooth day to day Sales Engineering workflows for the team.

Perform quota related data preparation to back forecasting, planning, and allocation activities across Sales Engineering.

Handle day to day Salesforce and Vivun issues at the first line to limit operational disruption for Sales Engineers.

Elevate complex or strategic issues to the SE Systems & Enablement lead to guide timely resolution and long term direction.

Extend AI enabled workflows to improve operational efficiency and insight generation for Sales Engineering.

Sustain and improve existing SFDC and Vivun processes to support the Sales Engineering team.

Own data quality checks and process documentation so that operations remain transparent and auditable.

Collaborate with cross functional partners to align data definitions, metrics, and reporting standards.

Monitor dashboard usage and stakeholder feedback to iterate on visualizations and insights.

Support ad hoc analysis requests that help Sales Engineering understand performance and pipeline health.

Contribute to documentation and best practices that make operations repeatable and scalable.

Participate in data interviews, SQL exercises, and case studies as part of continuous improvement cycles.

Requirements

You must work in Romania under local employment and visa rules.

You must have strong Salesforce skills and reporting capabilities tailored to Sales Engineering needs.

You must have hands on experience with AI tools and a willingness to build AI skills for operational and reporting work.

You must be able to own complex data tasks end to end with minimal supervision.

You must communicate clearly with both technical and non technical stakeholders in a fast paced environment.

You must be comfortable working with databases, queries, and dashboards to derive insights.

You must demonstrate attention to detail and reliability in delivering accurate data and reports.

Nice to have

Experience in sales operations, sales engineering, or a similar cross functional role.

Familiarity with Power BI, SQL, and data modeling concepts.

Background in data analysis, data science, or data engineering fundamentals.

Understanding of quota, pipeline, and forecasting concepts in a SaaS environment.

Experience collaborating with Salesforce and internal tooling teams.

Practical notes

The role is based in Romania as a permanent position within the Sales Engineering organization.

You will support Sales Engineering through data, tools, and day to day operations alongside SalesOps and leadership.

Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study.

Candidates may be asked to design a metric, interpret an experiment, or build a small model.

Some companies give a take home analysis.

Expect questions about past projects and the business impact of your work.

Interviewers often evaluate how you communicate uncertainty and business impact, not only the math.

Bringing a clean write up of a past analysis to the interview is well received.

Good to know

Questions to ask

Good questions to ask the employer in the interview include what does success look like in the first six months, how is the team structured, what is the current biggest challenge, and how are decisions made.

Asking about growth paths and the review process is also well received.

Employers expect questions, and good ones show preparation.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead.

Many professionals specialize in machine learning, analytics, or infrastructure.

Cross functional work with product and engineering teams becomes more important at senior levels.

The field changes quickly, so continuous learning is part of the job.

Professionals who can translate numbers into decisions tend to advance fastest.