Senior Analyst, Data Modeling

Royal Caribbean Cruises Ltd•Miami, FL

85d•Onsite

About The Position

The Senior Analyst, Data Modeling designs, develops, and manages data solutions that support Royal Caribbean Group’s goal of becoming a data-driven organization. The role works with internal stakeholders across areas including Product Management, Revenue, Sales, Marketing, and Digital to deliver meaningful, high-quality data for downstream users and platforms, including data scientists, business and product analysts, reporting platforms, and automated processes. Working closely with Data Solutions Engineering and Data Engineering, the Senior Analyst architects data definitions and structures, designs logical and conceptual data models, and oversees the ETLs that populate them. The position gathers business requirements, maintains a consistent semantic layer of certified metrics and dimensions, performs analysis to answer business questions, and communicates findings and recommendations to stakeholders. It also helps define and enforce data platform standards and documents the metrics and infrastructure needed for consistent reporting across the organization. This is a full-time, onsite position based in Miramar, Florida. It reports to the Senior Manager, Data Solutions Engineering and is not eligible for work authorization sponsorship.

Requirements

At least 5 years of experience working with data in a cross-functional role such as analytics, business intelligence, data science, data warehousing, or product management.
Bachelor’s degree in a technical discipline such as information science, mathematics, computer science, or econometrics, or similar work experience.
Expert-level SQL skills, preferably in more than one dialect, with exposure to both SQL and NoSQL databases ideally.
Strong analytical skills, including the ability to collect, organize, analyze, and disseminate substantial amounts of information with attention to detail and accuracy.
Expertise in data modeling concepts, database design, and building or maintaining a semantic or metrics layer that feeds BI tools.
Experience with a modern cloud data warehouse or lakehouse, such as Databricks, Snowflake, BigQuery, or Redshift.
Proven ability to address the challenges of processing large, structured datasets, including datasets with tens of billions of rows.
Knowledge of tradeoffs among data storage approaches, including row-oriented versus columnar, relational versus non-relational, and structured versus unstructured.
Exposure to big data and real-time event data streaming.
Experience setting and ensuring data platform standards for data management, governance, and usage.
Strong experience in requirements engineering and the design, development, and deployment of ETL and data modeling solutions.
Broad skills and knowledge across software, systems, and solution development in multiple technical domains.
Strong communication skills, including the ability to socialize and influence proposed technology and solution architectures and gain support from business and IT communities.
Technical team leadership, mentorship, and collaboration skills, and the ability to build strong working relationships with end users, peers, and vendors.
Technical knowledge of Erwin or a similar tool; Oracle, SQL Server, Synapse, and Couchbase in cloud and on-premises environments; data warehouse modeling; and expert SQL.
Working knowledge of ETL concepts and tools such as Informatica Cloud, Informatica PowerCenter, ADF, and/or Talend.
Knowledge of reporting tools such as Microsoft Power BI, Hyperion, Oracle OBIEE, and Tableau.
Knowledge of data governance, including collecting, keeping, cataloging, and efficiently using data.

Responsibilities

Work directly with business stakeholders to understand their decision needs and translate them into clear data and reporting requirements.
Design logical and conceptual data models by defining entities, relationships, grain, and business rules, and analyze and interpret data for the enterprise data platform.
Define and enforce standards for the data platform.
Maintain the semantic layer by defining certified metrics and dimensions that have consistent meaning across the organization.
Perform ad hoc analysis, cohort and trend work, and investigative queries to answer specific business questions.
Partner with Data Engineering by documenting and handing off logical models, collaborating on physical implementation and testing, identifying the boundary between raw ingestion and modeled data, and flagging upstream data issues.
Define requirements for engineers developing data pipelines, database infrastructure, and instrumentation.
Present findings and recommendations to non-technical stakeholders in a way that supports decision-making.
Partner with product managers, business partners, and analysts to understand the business rules and needs the data model must support.
Use data management and analysis experience to identify improvements to the efficiency, reliability, and usability of organizational data.
Define and document underlying metrics and design infrastructure, including ETLs, table structures, and metadata, to enable consistent reporting.