Data Engineer

Description

Who will benefit

• Data Engineer
• Data Architect
• ETL Engineer
• Database Engineer
• Big Data Engineer
• Data Integration Engineer
• Anlytics Engineer
• Data Pipeline Engineer
• Data Operations Engineer
• Data Services Engineer

Key Features

Aligned to an industry approved standard it focuses on building capability through:

• Gain expertise in Python to improve data analysis, including Pandas and version control working with Gitlab
• Learn how to acquire data from internal and external sources, including APIs, unstructured data, and prepare it for advanced analysis
• Understand regulatory compliance surrounding data collection, storage and usage, including GDPR
• Master databases and SQL, including designing and modelling, application layers and data warehouse design
• Use code and no-code tools to develop data products and pipelines and master the process of Extract, Transform, Load (ETL)
• Understand the Product development and the Software Development Lifecycle (SDLC) and how this impacts data productionisation.

Time commitment

You’ll need time during working hours and must be confident you’re able to allocate this within your workload.

You’ll receive a plan of your journey, it’ll broadly consist of:

• Fixed time of approx 3 hours per week
• Flexible time of approx 3 hours per week

Activities you’ll complete

• Stretching workplace development
• Participating in webinars
• Self directed study
• Workplace reflections
• Assessments
• Building portfolio

BPP support

• Regular 1:1 coaching reviews
• Industry experienced Tutors
• Virtual learning environment ‘the Hub’

Progression opportunities

After completion, you can consider progressing onto another PCDP such as:

• Digital and Technology Solutions Level 6
• MSc Applied Data Analytics Level 7