Apply now
Master'sBusiness, Management & Technology Policy Applications open

Master’s in Data, Digital & Platforms Engineering

Online master's programme of 1,900 hours in data, digital and platform engineering. 12 modules covering platform economics, data architecture, data pipelines, data governance and quality, EU data regulation, machine learning, data products, platform strategy, digital transformation and data security, with an assigned technical tutor.

Duration1900 h
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
Enrol now View syllabus Ask an advisor
Virtual lab · try it before you enrol

Take the controls.

Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.

SIM · LIVE ●
Learning outcomes

What you will be able to do

01

Analyse platform economics and network effects.

02

Design scalable cloud data architectures.

03

Build batch and real-time data pipelines.

04

Implement data governance and data quality programmes.

05

Apply European regulation on data and digital platforms.

06

Develop user-centred data products.

07

Assess the economic value and risks of data initiatives.

Profile

Who it's for

  • Data engineers who want to move from pipelines to full platform design.
  • Data architects working on lakehouse, data mesh or cloud migrations.
  • Data product managers responsible for dashboards, APIs and data services.
  • Data governance leads building quality and compliance programmes.
  • Digital transformation consultants advising on data strategy.
  • Digital business analysts looking to deepen their technical grounding.
  • Engineering or science graduates seeking to enter the technology and software sector.
Standards & tools

What you'll work with

  • Regulation (EU) 2016/679 (General Data Protection Regulation)
  • Regulation (EU) 2023/2854 (Data Act)
  • Regulation (EU) 2022/868 (Data Governance Act)
  • Regulation (EU) 2022/1925 (Digital Markets Act)
  • Regulation (EU) 2022/2065 (Digital Services Act)
  • ISO/IEC 27001:2022 (information security management)
  • ISO/IEC 25012:2008 (data quality model)
  • ISO 8000-8:2015 (data quality)
  • Apache Airflow
  • dbt
  • Apache Kafka
  • Apache Spark
  • Power BI
Syllabus

12 modules · 1900 h

  1. 1.1Data as an economic asset
  2. 1.2Direct and indirect network effects
  3. 1.3Two-sided and multi-sided markets
  4. 1.4Platform business models
  5. 1.5The attention economy and the data economy
  6. 1.6Data value chains
  7. 1.7Monetisation and pricing on platforms
  8. 1.8Industrial versus consumer platforms
  9. 1.9Platform growth metrics
  10. 1.10Case study: analysis of a mobility platform
  1. 2.1Conceptual, logical and physical models
  2. 2.2Data warehouses and dimensional modelling
  3. 2.3Data lake and lakehouse architecture
  4. 2.4Data mesh and data domains
  5. 2.5ETL and ELT integration
  6. 2.6Batch and stream processing
  7. 2.7Catalogues and metadata
  8. 2.8Cloud reference architectures
  9. 2.9Selecting storage technologies
  10. 2.10Workshop: designing a dimensional model
  1. 3.1Ingestion from operational databases
  2. 3.2Change data capture (CDC)
  3. 3.3Orchestration with Apache Airflow
  4. 3.4Transformations with dbt
  5. 3.5Streaming with Apache Kafka
  6. 3.6Distributed processing with Spark
  7. 3.7Partitioning and columnar formats
  8. 3.8Pipeline observability
  9. 3.9Automated data testing
  10. 3.10Lab: end-to-end pipeline with Airflow and dbt
  1. 4.1The DAMA-DMBOK reference framework
  2. 4.2Data owner and data steward roles
  3. 4.3Quality dimensions according to ISO/IEC 25012
  4. 4.4Profiling and validation rules
  5. 4.5Master data management
  6. 4.6Lineage and traceability
  7. 4.7Retention and archiving policies
  8. 4.8Data governance committees
  9. 4.9Data management maturity
  10. 4.10Case study: quality programme at an insurer
  1. 5.1The General Data Protection Regulation
  2. 5.2The Data Governance Act
  3. 5.3The Data Act and access to IoT data
  4. 5.4The Digital Markets Act and gatekeepers
  5. 5.5The Digital Services Act
  6. 5.6Common European data spaces
  7. 5.7International data transfers
  8. 5.8Privacy impact assessments
  9. 5.9Pseudonymisation and anonymisation
  10. 5.10Practical exercise: impact assessment of a connected product
  1. 6.1Life cycle of an analytics project
  2. 6.2Feature engineering
  3. 6.3Supervised classification models
  4. 6.4Time series and demand forecasting
  5. 6.5Segmentation and unsupervised learning
  6. 6.6Recommender systems
  7. 6.7Model explainability
  8. 6.8Deployment with MLOps
  9. 6.9Monitoring model drift
  10. 6.10Lab: forecasting model with scikit-learn
  1. 7.1Product thinking applied to data
  2. 7.2Discovering user needs
  3. 7.3Data contracts between teams
  4. 7.4Data APIs and versioning
  5. 7.5User experience in dashboards
  6. 7.6Experimentation and A/B testing
  7. 7.7Adoption and value metrics
  8. 7.8Data product roadmap
  9. 7.9Cross-functional product teams
  10. 7.10Workshop: specifying a data product
  1. 8.1Designing core interactions
  2. 8.2Launch and the chicken-and-egg problem
  3. 8.3Governance of complementors
  4. 8.4Openness versus platform control
  5. 8.5Open APIs and external developers
  6. 8.6Competition between platforms
  7. 8.7Interoperability and standards
  8. 8.8Envelopment strategies
  9. 8.9Famous platform failures
  10. 8.10Case study: strategy for a B2B marketplace
  1. 9.1Digital maturity assessment
  2. 9.2Digital operating models
  3. 9.3Managing cultural change
  4. 9.4Agile methodologies at scale
  5. 9.5Organising data teams
  6. 9.6Data literacy
  7. 9.7Managing technology suppliers
  8. 9.8Transformation indicators
  9. 9.9Funding digital initiatives
  10. 9.10Practical exercise: transformation roadmap
  1. 10.1Information classification
  2. 10.2Role-based and attribute-based access control
  3. 10.3Encryption at rest and in transit
  4. 10.4ISO/IEC 27001 management systems
  5. 10.5Privacy by design
  6. 10.6Algorithmic bias and fairness
  7. 10.7Responsible use of personal data
  8. 10.8Data breach response
  9. 10.9Algorithm auditing
  10. 10.10Case study: responding to a customer data breach
  1. 11.1Valuation methods for data assets
  2. 11.2Business cases and return on investment
  3. 11.3Total cost of ownership in the cloud
  4. 11.4FinOps and cost optimisation
  5. 11.5Data exchanges and marketplaces
  6. 11.6Data-sharing alliances
  7. 11.7Data as a service
  8. 11.8Platform financial indicators
  9. 11.9Risks and liabilities associated with data
  10. 11.10Workshop: business case for a data product
  1. 12.1Choosing the data challenge
  2. 12.2Needs and stakeholder analysis
  3. 12.3Design of the proposed architecture
  4. 12.4Prototype implementation
  5. 12.5Validation with real data
  6. 12.6Regulatory and ethical assessment
  7. 12.7Associated business model
  8. 12.8Deployment plan
  9. 12.9Master's Thesis report
  10. 12.10Master's Thesis: defence before a panel
Methodology, software & labs

How you'll learn

The programme is delivered 100% online on the SEIUM campus, with downloadable materials, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, so you can combine it with your job.

Each module combines theory, guided reading of the original regulations and standards, and applied exercises. The working software is Apache Airflow, dbt, Apache Kafka, Apache Spark and Power BI, in free, academic or evaluation versions. Exercises are based on real procedures: designing an end-to-end data pipeline, carrying out a data protection impact assessment for a connected product and defining the business model of a digital platform. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: Regulation (EU) 2016/679 (GDPR), Regulation (EU) 2023/2854 (Data Act), Regulation (EU) 2022/868 (Data Governance Act), Regulation (EU) 2022/1925 (Digital Markets Act), Regulation (EU) 2022/2065 (Digital Services Act), ISO/IEC 27001:2022, ISO/IEC 25012:2008 and ISO 8000-8:2015.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of the GDPR, the Data Act and the Data Governance Act applied to real cases, not as a theoretical annex.
Industry software:Apache Airflow, dbt, Apache Kafka, Apache Spark and Power BI, in free, academic or evaluation versions.
Master's Thesis with defence:a data platform prototype validated with real data, with its regulatory assessment, business model and deployment plan, defended before a panel.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Dimensional data model
Design a dimensional model for an enterprise data warehouse.
End-to-end data pipeline
Build a complete pipeline orchestrated with Airflow and transformed with dbt.
Connected product impact assessment
Carry out a data protection impact assessment for a connected product.
Data product business case
Specify a data product and build its business case.
Master's Thesis
A data platform prototype validated with real data, with its regulatory assessment and business model, defended before a panel.
Career opportunities

Where this takes you

Data engineerData architectData product managerData governance leadDigital transformation consultantDigital business analyst
Admissions & scholarships

Requirements

  • A university degree in engineering, science or architecture, or equivalent demonstrable professional experience.
  • An internet connection and a computer capable of running the programme's software.
  • English sufficient to read technical standards.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

Apply now Scholarships
FAQ

Frequently asked questions

The programme totals 1,900 hours over 12–14 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with Apache Airflow, dbt, Apache Kafka, Apache Spark and Power BI, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute diploma, awarded on passing the continuous assessment and the Master's Thesis. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.

Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.

A university degree in engineering, science or architecture (or equivalent professional experience), a computer able to run the software and English sufficient to read technical standards.

Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.

Keep exploring

Related programmes

See all in Business, Management & Technology Policy
Master's11,090 $

Master’s in Global Logistics Operations Engineering for Teams and Series

Online master's programme of 1,900 hours in global logistics operations engineering for teams and series. 12 modules covering motorsport logistics, air, sea and road transport, customs…

1900 hOnlineBusiness, Management & Technology Policy
Master's9,970 $

Master’s in Design to Cost / Design to Value

Online master's programme of 1,900 hours in design to cost and design to value. 12 modules covering value analysis methodology, functional analysis, cost estimation, DFMA, target…

1900 hOnlineBusiness, Management & Technology Policy
Master's6,610 $

Master’s in Leasing, Asset Finance and Residual Value Management Engineering

Online master's programme of 1,900 hours in leasing, asset finance and residual value management engineering: 12 modules covering lease accounting and tax, deal structuring and pricing,…

1900 hOnlineBusiness, Management & Technology Policy
Master’s in Data, Digital & Platforms EngineeringEnrol
Related programmes

More in Business, Management & Technology Policy

Master’s in Business Digital Twins EngineeringView programme →Master’s in Technical Procurement and Supplier Development EngineeringView programme →Master’s in Events, Leagues & Motorsport Governance Engineering (complementary)View programme →Master’s in Incubation/Spin-off Engineering from Universities and Technology CentresView programme →Master’s in Technology and Data Contracting EngineeringView programme →Master’s in Leasing, Asset Finance and Residual Value Management EngineeringView programme →

All Business, Management & Technology Policy programmes → · How to apply · Fees & financing · Scholarships

Scroll to Top
SEIUM – Institute of Advanced Engineering
Powered by  GDPR Cookie Compliance
Privacy Overview

This website uses cookies so that we can provide you with the best user experience possible. Cookie information is stored in your browser and performs functions such as recognising you when you return to our website and helping our team to understand which sections of the website you find most interesting and useful.