Data Engineer - The Emirates Group

Job Purpose: The Data Engineer is a fully participating member of a cross-functional team working autonomously on technology development and problem resolution in the Enterprise Data & Analytics space. The role involves the development, testing, implementation, support, and maintenance of technical data and analytic solutions and products that support Emirates Airlines and the Emirates Group businesses.

In this role you will:

1. Complete data engineering coding tasks on problems of low to moderate scope and complexity. Demonstrate good coding principles. Ensure solutions are reviewed to adhere to published data privacy and cybersecurity principles.
2. Debug issues of complexity, resolve blockers, and follow design documents with some supervision.
3. Assist with data analysis activities such as source system analysis, data modeling, data dictionary collection, data profiling, and source-to-target mapping to ensure solutions deliver on business needs.
4. Work on problems of diverse scope where analysis of data requires evaluation of identifiable factors. Demonstrate good judgment in selecting methods and techniques for obtaining solutions.
5. Work with senior and lead data engineers in the technical design process by contributing to the analysis of data application, data integration, large data storage, or data pipeline requirements.
6. Update data inventories and registries as required to keep metadata and data lineage up-to-date following agreed Data Governance standards, guidelines, and principles.
7. Carry out unit testing with some supervision. Troubleshoot issues and fix defects that are of low to moderate complexity.
8. Shadow senior and lead data engineers on design and architecture components and collaborate with members of the cross-functional team to identify areas of inefficiency and propose solutions.
9. Adhere to published coding standards, guidelines, and best practices and contribute to Data Engineering Playbooks and other data technology blueprints.
10. Proactively work with senior and lead data engineers, chapter leads, and line managers to upskill on the technology set used by Data Engineering.

Qualifications & Experience: To be considered for the role you must meet the below requirements:

1. 12 years of schooling or equivalent. Consideration will be given to those with 123 or equivalent in a relevant field such as Computer Science, Computational Mathematics, Computer Engineering, or Software Engineering.
2. Specialization or electives in a Data & Analytics field (e.g., Data Warehousing, Data Science, Business Intelligence) are nice to have.
3. Experience: 4 years in Data Engineering (Fewer years of experience will be considered for Master’s degree holders).
4. Minimum 1 year of development, testing, and support experience in analytic applications such as MIS repositories, Data Lakes, or Data Warehouse.
5. Some experience with batch data ingestion and experience with coding pipelines that handle massive quantities of data securely and in a timely fashion.
6. A rudimentary understanding of reading data models is required; an understanding of data vault or dimensional modeling is nice to have.
7. Ability to conduct unit testing. Good SQL querying skills required; airline industry experience a nice-to-have.

Knowledge/Skills:

1. The ability to conduct data analysis (e.g., data dictionary/metadata collection, data profiling, source-to-target mapping) is preferred.
2. Operates with a You Code It, You Own It mindset (i.e., supports the products they build).
3. Team player able to collaborate with others to remove blockers, solve complex design problems, and debug/resolve issues.
4. Able to deliver solutions (and associated value) interactively.
5. Is accountable and displays a positive attitude.
6. Self-starter and has a passion for exploring and learning new technologies, especially those in the Enterprise Data & Analytics space.

Technology Domain Key Technologies/Tools:

1. Big Data: Hadoop, Spark, Scala, Hive, HBase, Sqoop, Oozie, Apache Nifi, Airflow, HDFS, ADLS (Gen 2), Azure Data Factory (ADF), DataBricks, Kafka, Elasticsearch, AVRO / PARQUET file formats, Data Analysis.
2. Modeling and Reporting: Snowflake, SQL, Data Vault 2.0, MicroStrategy, Power BI.
3. Cloud Technologies: Microsoft Azure and Cloudera technology stacks.
4. Integration and Messaging: Streaming (e.g., Spark Streaming), SnapLogic, TIBCO, Kafka.
5. CI/CD: GIT, Bitbucket, Azure DevOps, Jenkins, JIRA, Confluence.
6. Automation: Java, Selenium, AppDynamics, HP Load Runner, Jmeter, Python, Automation Anywhere.