Azure Data Engineer

Job Requirements

A data engineer works on implementing complex data projects with a focus on collecting, parsing, managing, analysing and visualising large sets of data to turn information into value using multiple platforms.

You will work with business analysts, architects and data scientists to understand customer business problems and needs, secure the data supply chain, implement analysis solutions and visualise outcomes that support improved decision making for a customer.

• Working with colleagues to understand and implement data product requirements
• Delivering the data supply chain, understanding how data is ingested from different sources and combined / transformed into a single data set
• Understanding how to analyse, cleanse, join and transform data
• Implementing designed / specified solutions into the chosen platform with due consideration for Data Ops principles
• Working with colleagues to ensure that the cloud infrastructure available is capable of meeting the solution requirements
• Planning, designing and conducting tests of the implementations, correcting errors and re-testing to achieve an acceptable result
• Appreciate how to manage the data including security, archiving, structure and storage
• Support solution architect in creating architecture deliverables such as data architecture, data processing architecture, data testing strategy

• Degree level education in Mathematics, Scientific, Computing or Engineering discipline or equivalent experience
• Mandatory
• 6+ years of experience of Data Engineering roles including 2+ years in leading end-to-end design and implementation of data solutions for large scale use cases
• Experience in designing solutions using databases and data storage technology using Azure data management components
  - Azure SQL, ADLS, Cosmos
• Experience building, optimizing and automating data pipelines, architectures and data sets using MS Azure data management and processing components through IaaS/PaaS/SaaS implementation models implemented through custom solutions
• Proficiency in Python using different modules used for data munging
• Be up to date with data processing technology / platforms such as Spark (Databricks)
• Experienced in Azure Dev Ops
• Desirable
• Experience of working in tightly regulated industry is desirable
• Experience with ETL and/or data integration tool such as Informatica, Datastage, SSIS is highly desirable
• Good understanding of Azure infrastructure components and their fit in different types of data solutions

A data engineer works on implementing complex data projects with a focus on collecting, parsing, managing, analysing and visualising large sets of data to turn information into value using multiple platforms.

You will work with business analysts, architects and data scientists to understand customer business problems and needs, secure the data supply chain, implement analysis solutions and visualise outcomes that support improved decision making for a customer.

• Degree level education in Mathematics, Scientific, Computing or Engineering discipline or equivalent experience
• Mandatory
• 6+ years of experience of Data Engineering roles including 2+ years in leading end-to-end design and implementation of data solutions for large scale use cases
• Experience in designing solutions using databases and data storage technology using Azure data management components
  - Azure SQL, ADLS, Cosmos
• Experience building, optimizing and automating data pipelines, architectures and data sets using MS Azure data management and processing components through IaaS/PaaS/SaaS implementation models implemented through custom solutions
• Proficiency in Python using different modules used for data munging
• Be up to date with data processing technology / platforms such as Spark (Databricks)
• Experienced in Azure Dev Ops
• Desirable
• Experience of working in tightly regulated industry is desirable
• Experience with ETL and/or data integration tool such as Informatica, Datastage, SSIS is highly desirable
• Good understanding of Azure infrastructure components and their fit in different types of data solutions