Lead Data Engineer

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

Data Analytics and AI

team has an exciting career opportunity for a full time

Lead, Data Solutions Engineer

reporting to the Senior Manager, Data Solutions Engineering

This position will work on-site in Miramar, Florida.

Position Summary:

The

Lead Data Solutions Engineer

is responsible for leading the delivery, managing and operationalizing data solutions pipelines in support of data and analytics use cases using appropriate methodologies, technologies including extract, transform and load of data. This role will focus on building Revenue data products. Additionally, this role will contribute to the technical solutioning and building of our Data as a Service capabilities. The Lead Solutions Data Engineer will use both technical and analytical skills to understand and solve business problems using available resources and current technology stack, while ensuring data governance and data security compliance. The lead will mentor junior engineers in finding optimal and efficient solutions for designing, preparing, and storing data for analytical and operational use cases. The role is expected to drive/lead the delivery of one or more data solution components/products from project inception through its delivery in close collaboration with cross-team members, which includes functional and technical resources.

Essential Duties and Responsibilities:

Leads data engineering development projects which include the design of complex flexible, durable, reusable and scalable data pipelines and frameworks to automate the ingestion, processing and delivery of both structured and unstructured batch and real-time streaming data employing a variety of data integration and data preparation tools for on premises and cloud for different business scenarios.
Lead collaborative team working with other areas to ensure that all solutions are complete, operable, conform to business processes, and meet the business needs with the agreed quality of service
Evaluates ETL/ELT best practices within the industry to learn principles and applications and drive development adoption and trains others
Design, maintain, and build event-based, real-time, and micro-batch data pipelines utilizing a variety of data integration and data preparation tools
Design and implement high-frequency data science model results for operational purposes.
Create solutions for: (1) analytics and data science to provide the ability to uncover the answers to major questions that help organizations make objective decisions and/or gain a competitive edge (2) operational reports, key performance indicators/metrics, or other insights into current organizational activities.
Leads data analysis required to troubleshoot data related issues and assist in the resolution of data issues.
Collaborates with analytics and business teams to improve data models that feed business intelligence tools, increasing data accessibility and fostering data-driven decision making across the organization
Leads requirements gathering.
Anticipates any obstacles and makes plans accordingly to ensure that stakeholder requirements are fully reflected in data architecture outcomes.
Collaborate with the Enterprise Architecture team to ensure alignment on data standards and processes.
Work with data and analytics experts to strive for greater functionality in data systems.
Coordinates data architecture project resources to effectively achieve business goals.
Solves complex issues in target data architecture design processes (e.g. data management process modeling, data entity matrix design).
Ensure the end-to-end solution is fit for purpose, meet the needs of business, the agreed requirements, and are both pragmatic and supportive of the strategic architecture direction
Position requires on-call and off-hours support.
Mentors and coaching other team members, cross-trains on features and techniques of new data integration, data warehousing and data lake technologies.
Financial Responsibilities:

Significant understanding of effort estimation activities. Will lead large/complex estimation activities.
Drives expectations in meeting deadline within budget, schedule and appropriate quality.
Qualifications:

Bachelor or Master of Science in Engineering, Computer Science, Information Technology or equivalent
10+ years of experience in Data Warehouse design and data modeling patterns (relational and dimensional)
10+ years of experience with ETL tool development such as Informatica or Talend or ADF
Must have strong analytical skills for effective problem solving
Ability to work independently, handle multiple tasks simultaneously and adapt quickly to change with a variety of people and work styles.
Must be capable of fully articulating concisely technical concepts to non-technical audiences.
Knowledge and Skills:

Extensive ability to design, build and manage data pipelines for data structures encompassing data transformation, data models, schemas, metadata and workload management.
Extensive experience with popular programming languages including SQL, PL/SQL, T-SQL, others for relational databases
Extensive experience and hands-on experience with scripting languages: Python, Pyspark, Scala, Java, etc.
Extensive experience in one of the following tools: ADF, Informatica, Databricks
Extensive experience with technologies: Databricks Lakehouse; Oracle, MSSQL, Hadoop and NoSQL
Extensive experience designing and building metadata driven frameworks.
Extensive experience with various Data Management architectures like Data Warehouse, Data Lake and the supporting processes like Data Integration, Governance, Metadata Management
Extensive experience in working with large, heterogeneous datasets in building and optimizing data pipelines, pipeline architectures and integrated datasets using traditional data integration technologies. These should include [ETL/ELT, data replication/CDC, message-oriented data movement] and data ingestion and integration technologies such as stream data integration, and data virtualization.
Extensive experience in working with and optimizing existing ETL processes and data integration and data preparation flows and helping to move them in production
Extensive experience writing and optimizing advanced SQL queries in a business environment with large-scale, complex datasets
Extensive experience of data warehousing and delta lake best practices within the industry
Extensive experience of various development and deployment environments on-premises, in the cloud and across multi-cloud environments such as: AWS, Azure, Google
Experience developing reference architecture, principles and standards
Strong knowledge of relational and dimensional data modeling patterns.
Strong knowledge of the essential elements of data architecture, platforms and products.
Physical Demands:

The physical demands described here are representative .