Základné informácie
Ref Number
Posledný deň na podanie prihlášky
Primárna lokácia
Krajina
Typy zamestnania
Work Style
Opis a požiadavky
Work within the existing data engineering team to develop, test and maintain data pipelines, ETL and data architectures.
Gather data requirements from multiple sources and develop new data plans/ pipelines to ensure optimized data flows can feed business reporting
Responsible for independently developing end to end data flows, data models across multiple complex / large data sets
Gain an understanding of our data architecture and demonstrate innovative solutions to improve complex workflows.
Work cross functionally with the Business Intelligence and Analytics team to design data models that can support these teams.
Ďalší popis práce
Candidate must have proven strong skills/experience (minimum 5 years) using relational databases MySQL,SQL, etc years
Ability to design, develop and deploy complex data models, supported by quality controlled data pipelines and ETL processes
Must have proven skills in developing relational data models.
Ability to optimize data retrieval through good design - solving complex data problems.
Candidate should have experience using BigQuery/GCP (2-3 years)
Candidate must possess excellent communication skills, be a self-starter, where role requires proven ability to manage projects to agreed timelines and deliverables
Experience / Education :
Prior experience as a data engineer, developing data models for complex data sets
Minimum of 5 Years of experience developing data pipelines and working with relational databases
Degree in Computer Science, IT, or similar field
Strong and in depth understanding of relational databases - MS SQL, My SQL - minimum for 5 years
Experience with Python, GIT and similar languages & applications used for data pipelines / management
Candidate should have experience using BigQuery/GCP (2-3 years)
Previous experience in implementation of ETL applications, Data warehousing/ data modeling principles, architecture and its implementation in large environments
EEO Statement