Degree or Honours (12+3 or equivalent) in a relevant field such as Computer Science, Computational Mathematics, Computer Engineering, or Software Engineering.
Specialization or electives in a Data & Analytics field (e.g. Data Warehousing, Data Science, Business Intelligence) is a nice-to-have.
Experiences
5+ years in Data Analysis experience (fewer years’ experience will be considered for Masters degree holders).
Minimum 5+ years conducting data analysis tasks (e.g. source system identification, data dictionary/metadata collection, data profiling, source-to-target mapping).
Proven track record developing and maintaining data documentation artifacts in organizations of similar size and complexity within project deadlines.
Proven track record developing templates for organizing data analysis output.
Deep expertise in business transformation rules and understanding of data solution designs.
Experience building conceptual, logical, and physical data models.
Experience writing complex SQL queries to analyze data and provide results to business users or project team members.
Experience with building information designs for data-centric projects on two or more of the following (preferably within the airline industry): Data Warehouses, Big Data Environments for Analytics, Data API, Business Intelligence Solutions.
Exposure to data governance or business intelligence tools (e.g. Collibra, Snowflake, Microstrategy, Power BI) is a nice-to-have.
Airline industry experience strongly preferred (or expertise in a supporting function such as HR or Finance); needs to understand the business domains well to conduct data modeling and analysis activities.
Knowledge/Skills
Excellent communicator; able to communicate rationale, approaches, and complex data models to business stakeholders, peers, and management.
Operates with a “You Code It, You Own It” mindset (i.e. supports the products they build).
Demonstrated problem-solver; able to design and document solutions independently.
Strong work ethic, being results-oriented, and accuracy/attention to detail are critical.
Demonstrated initiative; able to work both independently and as a team member.
Team player; able to collaborate with others to remove blockers, solve complex data problems, and debug/resolve issues.
Self-starter and has a passion for exploring and learning new technologies, especially those in the Enterprise Data & Analytics space.
Able to deliver solutions (and associated value) iteratively.
Is accountable and displays a positive attitude.
Key Technologies/Tools
Big Data & Distributed Processing: Querying HDFS or ADLS file storage systems, with tools such as Hive or H-Base, ElasticSearch; experience with AVRO / PARQUET file formats is nice-to-have.
Data Analysis, Modelling and Reporting: SQL, Snowflake, Data Vault 2.0, MicroStrategy, Power BI.