Analyst - IT Data Analytics

Role overview

Desired background

• The posting calls for advanced SQL and analytical skills, plus hands-on experience with martech data flows, CDP/CRM ecosystems, and data quality frameworks.
• Candidates should understand data movement from source systems through integration and warehousing to marketing activation.

Analytics and marketing intelligence

• Develop and maintain analytical models, dashboards, and reports for CRM, player development, and marketing operations.
• Analyze segments, campaign response, channel attribution, and platform use; report campaign lift, conversion, and engagement.
• Validate audience definitions and contact data against enterprise warehouse records; document query methods and assumptions.
• Identify patterns in guest engagement, transaction, and survey data and present clear findings to stakeholders.

Data quality and governance

• Create data quality rules and measurable thresholds for CDP and martech source systems and domains.
• Build monitoring pipelines, scorecards, and escalation alerts; validate eligibility, suppression, and segment membership before campaigns.
• Investigate data quality failures, coordinate remediation with data engineering, and maintain a rule catalog.
• Contribute validation logic for guest identity resolution, deduplication, match confidence, and profile completeness.

Data modeling and collaboration

• Participate in dimensional and relational modeling for guest profiles, gaming activity, hospitality transactions, engagement history, and feedback.
• Contribute to warehouse models using star and snowflake schemas; document grain, keys, measures, and SCD strategies.
• Collaborate on CRM and martech integration schemas and review models for analytical and activation needs.
• Partner with the martech team on data validation, campaign measurement, audience quality assurance, and analytics requests.

Requirements

• Have advanced SQL and analytical skills.
• Have hands-on experience with martech data flows, CDP/CRM ecosystems, and data quality frameworks.
• Understand how marketing data flows from source systems through integration and warehousing into activation.
• Be able to translate data-flow understanding into defensible analysis and measurable quality standards.
• Be able to document analytical methodology and assumptions for deliverables.