Sr. Analyst, Data Modeling

Royal Caribbean Cruises Ltd•Miami, FL

3d•Onsite

About The Position

Journey with us! Combine your career goals and sense of adventure by joining our exciting team of employees. Royal Caribbean Group is pleased to offer a competitive compensation and benefits package, and excellent career development opportunities, each offering unique ways to explore the world. The Royal Caribbean Group’s Analytics Team has an exciting career opportunity for a full-time Sr. Analyst, Data Modeling reporting to the Sr Manager , Data Solutions Engineer. The position is onsite and based in Miramar, Florida. Position Summary: The Senior Data Modeler is responsible for both the theory and practice of making RCG a data-driven organization. In this role, the Senior Data Analyst/Modeler will design, develop and manage data solutions for use by internal stakeholders in all areas of RCG including Product Management, Revenue, Sales, Marketing, Digital, to name a few. Working closely with Data Solutions Engineering, he or she will architect data definitions and structure and oversee the ETLs that populate them in order to deliver meaningful, high-quality data to downstream users and platforms, including data scientists, business and product analysts, reporting platforms, and automated processes.

Requirements

Strong experience in requirements engineering and the design, development, and deployment of ETL and Data Modeling.
A broad set of skills and knowledge including software, systems and solution development spanning multiple technical domains
Strong communication skills including the ability to socialize, influence and gain support for proposed technology and solution architectures from the business and IT community.
Technical team leadership, mentorship, and collaboration as well as the ability to cultivate strong working relationships with end-users, peers, and vendors
5+ years of experience with data in a cross-functional role (analytics, business intelligence, data science, data warehousing, product management)
Bachelor’s degree or similar work experience in a technical discipline (information science, mathematics, computer science, econometrics, etc.)
Expert-level skills in SQL (preferably in more than one dialect), ideally with exposure to both SQL and NoSQL databases
Strong analytical skills with the ability to collect, organize, analyze, and disseminate significant amounts of information with attention to detail and accuracy
Expertise in data modelling concepts, database design, building or maintaining a semantic/metrics layer that feeds BI tools
Experience with a modern cloud data warehouse or lakehouse (e.g., Databricks, Snowflake, BigQuery, Redshift).
Proven ability to overcome the challenges of processing large, structured datasets (tens of billions of rows)
Well-versed in the tradeoffs of different data storage approaches (row-oriented vs columnar, relational vs. non-relational, structured vs. unstructured)
Exposure to big data and real-time event data streaming
Setting and ensuring data platform standards for the management, governance, and usage of data
Erwin or similar
Oracle, SQL Server, Synapse, Couchbase (cloud and on-prem)
DW modeling
Expert SQL
ETL concepts (working knowledge of Informatica Cloud, Informatica PowerCenter, ADF, and/or Talend)
Reporting tools (Microsoft Power BI, Hyperion, Oracle OBIEE, Tableau)
Data governance (knowledge collecting, keeping, cataloging, and using data efficiently)

Responsibilities

Gather and translate requirements — work directly with business stakeholders to understand what they're trying to decide, and turn that into clear data and reporting requirements.
Design logical and conceptual data models — define entities, relationships, grain, and business rules ahead of physical implementation. Analyze and interpret data to be modelled into the enterprise data platform.
Define and enforce standards for the data platform.
Maintain the semantic layer — define certified metrics and dimensions so the same term means the same thing everywhere it's used.
Deliver analysis and insight — run the ad hoc analysis, cohort/trend work, and investigative queries that answer specific business questions.
Partner with Data Engineering — hand off logical models with clear documentation, and collaborate on how they get physically implemented and tested. work with Data Engineers on the boundary between raw ingestion and modeled data, and flag upstream data issues early. Define requirements for engineers developing new data pipelines, database infrastructure and instrumentation.
Communicate findings — present analysis and recommendations to non-technical stakeholders in a way that drives decisions, not just describes data.
Partner with stakeholders — work with product managers, business partners and analysts to understand business rules to understand what the model needs to support, not just what it needs to store.
Draw on deep experience with data management and analysis to identify improvements and increase efficiency, reliability, and usability of data across the organization.
Define and document the underlying metrics and design robust infrastructure (ETLs, table structures, metadata) to enable consistent reporting on those metrics throughout the organization.