Build systems that collect less, protect what they hold, honour people's rights over their data, and meet privacy law by design rather than after the fact.
Estimated time: 15–26 months · At about 10 hours a week.
Learning path
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Explain privacy principles and apply them to a system.
Recognise personal and special-category data.
Produce a record of processing.
Hands-on projects and labs
Map the personal data flows of a small sample application end to end.
Write a plain-language privacy notice for that application.
Done when you can…
Explain the core privacy principles and the main laws' duties.
Produce a data map and a lawful-basis analysis for a system.
Beginner
Privacy principles
Purpose limitation, data minimisation, accuracy, storage limits, security and accountability underlie most privacy law. Learn the principles and the rest follows.
Personal data is broader than names: it includes identifiers, location, device IDs and anything that can single someone out. Special categories need extra care.
A DPIA works through what could go wrong for people from a new processing activity and how to reduce it. Run it early, when you can still change the design.
Pseudonymisation replaces identifiers but data can still be re-linked; true anonymisation is hard and, when done, takes data out of scope. Know the difference.
When you share personal data with processors and partners, contracts and checks must carry the duties with it. Cross-border transfers need extra safeguards.
Judge where differential privacy and other PETs fit.
Build privacy into machine learning systems.
Fold privacy duties into incident response.
Hands-on projects and labs
Apply a differential-privacy library to publish a statistic from a dataset.
Build a retention job that deletes personal data past its retention period.
Done when you can…
Choose and justify PETs for a realistic data-use case.
Show an automated control, such as retention-based deletion, working.
Advanced
Differential privacy
Add carefully calibrated noise so you can publish statistics without exposing individuals. Powerful but easy to misapply; understand the privacy budget.
Encryption and key separation limit who can read personal data, including your own staff and processors. It supports, but does not replace, the other controls.
Privacy engineering turns privacy principles and laws into the way systems are actually built: collecting only what is needed, keeping it only as long as needed, protecting it, and letting people see, correct and delete their own data.
The work ranges from writing privacy into designs and running impact assessments to applying privacy-enhancing technologies such as anonymisation, pseudonymisation and differential privacy. It sits between legal, product and security teams and needs a bit of all three.
Why it matters
Privacy law now carries real fines and real engineering requirements. Products need people who can meet those requirements without grinding development to a halt, and who can build privacy in rather than bolting it on.
Typical job roles
EntryPrivacy analystRuns assessments and tracks data subject requests.
Mid-levelPrivacy engineerBuilds privacy controls and PETs into systems.
SeniorPrivacy architectDesigns privacy across products and data flows.
SeniorData protection officerOversees compliance and is the regulator contact.
LeadHead of privacyOwns the privacy programme and strategy.
Prerequisites
How applications and data stores handle personal data.
Data security and encryption basics.
An idea of the privacy laws that apply where you operate.