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Data Governance Engineer

POSITION SUMMARY
The Data Governance Engineer position is a part of the Data Analytics & AI team and supports the RCG corporation project goals. This position collaborates with a team of governance, legal/privacy and business stakeholders, data analysts, architects, and information technology professionals to implement enterprise data quality solutions in direct support of Royal Caribbean Group's enterprise level objectives. The ideal candidate for this role has a Data Governance and Data Quality background with functional and technical expertise. 

 

This position’s main objective is to deliver developed data quality solutions using software tools and implement a standardized approach for query and algorithm automation for executing data quality improvements.

 

ESSENTIAL DUTIES AND RESPONSIBILITIES

  • Acquire data from data sources and maintain SQL queries.

  • Analyze, query, and manipulate data according to defined business rules and procedures.

  • Maintain and improve data completeness through deduplication operational processes.

  • Write stored procedures, creating scripts for data loads and upgra+D7des for data migrations and data validations.

  • Develop shell scripts to automate file manipulation and data loading procedures.

  • Install and configure server setup on system development lifecycle environments.

  • Ensure developed jobs migrate through the system development lifecycle environments successfully.

  • Monitor key product metrics, understand root causes of changes in metrics

  • Identify, analyze, and interpret trends or patterns in complex data sets and depict the story via dashboards or reports

  • Perform data quality validations to ensure data adheres to business needs and expectations

  • Extensively work on data extraction, transformation and loading data from various sources, such as, Oracle and SQL Server.

  • Identify areas of improvement in accordance with data quality business rules on data quality dashboards.

  • Design and execute data remediation measures.

  • Create and ensure adherence to data quality standards.

  • Deliver service in accordance with established SLAs.

  • Ensure that data is within proper privacy/legal compliance.

 

QUALIFICATIONS

  • BS Degree in Mathematics, Economics, Computer Science, Information Management or Statistics is a plus but not a must.

  • 3+ years of experience in a CDP, data warehousing or related data platform environment.

  • Qualified with Azure Cloud and Databricks technologies.

  • Experienced with Apache Spark, Python, Scala, and R.

  • Experienced with Databricks Delta Lakehouse technology to perform data ingestion and data processing for data quality initiatives.

  • Proficient with ad-hoc analyses using SQL queries and python data analysis packages (Pandas) 

  • Use technologies such as Kafka, Snowflake, Apache Spark

  • Strong analytical skills with the ability to collect, organize, analyze, and disseminate significant amounts of information with attention to detail and accuracy.

  • Adept at report writing, presenting, and verbal communication skills.

  • Ability to operate effectively as part of a project team or individually.

Knowledge and Skills

  • Knowledge regarding data models, database design development, data mining and segmentation techniques is preferred.

  • Experience building segments and with ETL processes.

  • Strong understanding of Marketing Technology tools (Email/Marketing automation system, Personalization tools, Analytics tools, tag management systems).

  • Experience delivering data-driven products for Marketing, Sales, Advertising, and/or Analytics use cases.

  • Experience with DMP or CDP, data strategy, real-time and offline data processing of multiple data sources.

  • Read and write code in a programming language such as Python.

  • Assist with data analysis and reporting tasks using SQL and data analysis techniques. 

  • Develop and maintain documentation of data quality rules, processes, and procedures. 

  • Contribute to data quality process improvement efforts. 

  • Collaborate with stakeholders to identify data quality requirements. 

 

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