Engineering Data Analyst, R&D Operations CDMX

Why We Have This Role

How Youll Find Success

• Focus on data products becoming indispensable. Stakeholders integrate your dashboards and metrics into their key decisions and come back asking for more. They trust your work because its accurate well-documented and clearly communicated.
• High standards: Your datasets are trustworthy. Your dashboards are accurate. Your documentation makes it easy for others to understand and use your work. You care about being right and catching mistakes before they become problems.
• Technical growth: Youre constantly learning new tools and techniques. You experiment with new approaches to make data more digestible. Youre comfortable with messy datasets and dont get stuck when things are ambiguous. You love learning about how and why the data is being used to make decisions and business and product context.

How Youll Grow

• Technical Depth: Youll develop expertise in SQL data modeling visualization and dbt. Youll work with modern data tools and large-scale datasets.
• Business Acumen: Youll learn how great products are built by partnering closely with Product Operations Product Management Engineering and UX. Youll understand what metrics actually matter and why.
• Problem-Solving: Youll tackle complex data challengesfrom reconciling inconsistent data sources to designing metrics that accurately measure user behavior. Youll learn to think like a data engineer and an analyst.
• Career Path: Strong performers can grow into analytics engineering roles (building dbt models and data infrastructure) or senior analyst roles (owning strategic metrics and cross-functional initiatives). We promote based on impact and technical growth.

Things Youll Do

• Build and maintain dashboards and datasets: Create Tableau dashboards that answer recurring business questions. Write SQL queries to build datasets that power analysis and reporting. Ensure data products are accurate performant and well-documented.
• Support key metrics and reporting:Help populate data for metrics reviews. Maintain critical company metrics like Customer Maturity. Build datasets that support Golden Paths measurement and product performance tracking
• Answer strategic questions: Conduct ad-hoc analysis to help stakeholders understand user behavior product performance or business trends. Partner with Data Scientists when deeper statistical analysis is needed.
• Define and develop new metrics: At more senior levels work with Product and Engineering stakeholders to identify what should be measured design the right metrics and build the infrastructure to track them over time.
• Partner with upstream data providers: Work with Engineering and Data Engineering teams to understand data sources identify data quality issues and define requirements for new signals or datasets. Hold partners accountable when needed.
• Enable stakeholders: Lead sessions to help teams understand and use the products you build. Write clear documentation that makes metrics and datasets accessible to non-technical users.
• Maintain data quality:Build processes and checks to ensure data assets are trustworthy. Catch errors before they impact decisions. Be the person who cares about getting it right.

What Were Looking For On Your Resume

• Education & Experience: Bachelors or Masters degree in analytical disciplines (Business Analytics Economics Finance Computer Science Engineering Statistics) or relevant work experience. 0-3 years of experience as a data analyst analytics engineer or related role (internships count).
• Technical skills: Strong proficiency in SQL. Familiarity with Python for data analysis. Experience with data visualization tools like Tableau PowerBI or Looker.
• Proactive problem-solving skills: Evidence of proactively identifying issues and digging in to understand root causes and fix them.
• Clear communicator: Experience explaining technical concepts to non-technical stakeholders. Evidence of strong written communication skills.
• High standards with humility: Evidence of being detail-oriented. Evidence of humility and knowing when to ask for help.
• Nice to have: Experience with dbt for data modeling. Understanding of data warehouses and query optimization. Experience with Git and reproducible workflows.

What You Should Know About This Team

The PXE Analytics & Data Science Team has 3 specialized pillars: Data Science Analytics and Analytics Engineering.

• Data Science focuses on deep research experimentation and predictive modeling.
• Analytics focuses on operational reporting and structured insights.
• Analytics Engineering focuses on building reliable datasets and data infrastructure.

You will work alongside a passionate group of experts in Seattle and Mexico City all dedicated to bringing the power of data to Qualtrics itself.

Our Teams Favorite Perks and Benefits

• Hybrid Work Model: We gather in the office three days a week to collaborate and work where we want the rest of the week.
• Career Action Planning: Personalized career planning to help you achieve your goals inside and outside Qualtrics.
• Wellness: Comprehensive benefits including a wellness reimbursement and mental health benefits.