Online professional certificate
Professional Certificate in Data Engineering for Business Analytics
Build dependable analytics data: define measures and access, create tested pipelines, monitor quality and hand a usable dataset to its consumer.
- Format
- Online, self-paced
- Study time
- Up to 1 month
- Curriculum
- 20 applied lessons
- Language
- English
Practical capability
Build analytics data that people can trace and trust.
Follow an analytics request from a business decision through authorized source use, a repeatable data flow and an evidence-backed handoff. Six capability groups organize the work you will practise.
Define the consumer, decision, grain, measures, freshness and acceptance evidence.
Confirm permitted fields, map order and return feeds, and specify safe keys and business meaning.
Write reviewable SQL and intake logic, then schedule runs, dependencies and retries.
Design tests that catch defects and compare curated measures with authorized source totals.
Release changes with rollback, observe freshness and quality, respond to incidents and trace lineage.
Document a metric interface, assess data-product readiness and give analysts a usable result.
Who this course is for
A practical route into analytics data engineering.
Designed for early-career and transitioning practitioners who need to build, test and explain the data supply behind business analysis.
The operating cycle
Move from a business request to an accepted data product.
Practise the sequence from the model role operating playbook using one connected synthetic order-and-return case.
Curriculum
Four modules. Twenty applied lessons.
Define the Data Product
Begin with the decision the data must support. You will clarify the consumer's question, identify the owner of business definitions and check which source fields may be used before drawing a target model. This keeps an apparently simple report request from becoming an unexplained collection of tables. Then map order and return feeds, specify grain and keys, and write SQL that preserves the intended meaning of a measure. By the end of the module you can explain where a number came from, why a join is safe and what still needs a business or access decision.
01 Turn a Business Question into a Data Contract
Define a data request with its consumer, decision, grain, freshness and acceptance evidence.
Five practical steps
- Name the decision and consumer
- Ask for the measure and grain
- Identify sources and freshness
- List unknowns and owners
- Write acceptance checks
Primary deliverable: request-and-acceptance contract.
02 Assess Source Access and Protect Sensitive Fields
Distinguish technical access from approved data use and record the named owner's bounded source-access decision.
Five practical steps
- List requested fields and purpose
- Classify sensitive fields
- Identify source and control owners
- Record allowed and pending access
- Set a stop condition
Primary deliverable: source-access decision record.
03 Map Source Feeds to a Usable Target
Map source fields, keys, corrections and update behavior into an analytics target with traceable lineage.
Five practical steps
- Inventory feed shapes
- Profile keys and record update and correction behavior
- Choose target grain
- Map fields and corrections
- Review lineage with owners
Primary deliverable: source inventory and mapping sheet.
04 Define Grain, Keys and Business Measures
Design a reviewable data model whose grain, joins and metric definitions prevent double counting.
Five practical steps
- State each entity's grain
- Choose stable keys and test join cardinality
- Define measures
- Record exclusions and timing
- Seek metric-owner review
Primary deliverable: grain-and-model design record.
05 Transform Data Reliably with SQL
Write and explain SQL joins, filters and aggregation that preserve the agreed target grain.
Five practical steps
- Define expected input rows
- Write grain-safe joins
- Apply dated filters explicitly
- Compare output counts and totals
- Record query assumptions
Primary deliverable: SQL transformation and review sheet.
Build and Verify the Pipeline
Turn the agreed model into a repeatable data flow. You will use an approved scripting option, build a reviewable pipeline and set triggers, dependencies and retry behavior suited to the actual refresh requirement. The examples show one workable path without assuming every employer uses the same language or platform. Next, design checks that fail on realistic defects and reconcile curated values to their source interval. You will learn to distinguish a successful job from a correct and usable analytical result, and to keep the evidence a reviewer needs before release.
06 Specify and Test a Reproducible Intake Utility
Specify, run and test a small approved-language intake utility with clear input, output and error handling.
Five practical steps
- Define input contract
- Choose an approved language
- Validate and normalize records
- Log rejected cases
- Make reruns reproducible
Primary deliverable: scripting ingestion utility specification.
07 Build a Reviewable Data Pipeline
Build a versioned ingestion-to-curation pipeline with explicit dependencies and recovery points.
Five practical steps
- Define stage inputs and outputs
- Connect transformations
- Version configuration
- Mark checkpoints and lineage
- Test a clean and failed run
Primary deliverable: pipeline job manifest.
08 Orchestrate Runs, Dependencies and Retries
Choose triggers, dependency checks and bounded retries that avoid silent duplicate processing.
Five practical steps
- Set the real refresh trigger
- Check prerequisites
- Bound retries and backoff
- Define idempotent rerun
- Escalate unmet freshness
Primary deliverable: orchestration dependency and retry plan.
09 Design Tests That Catch Broken Data
Write data tests that expose schema, key, join and value defects before release.
Five practical steps
- List plausible defects
- Choose assertions
- Prepare ordinary and edge-case data
- Run expected failures and investigate unexpected results
- Save release evidence
Primary deliverable: data-test case pack.
10 Reconcile Curated Measures to Source Records
Reconcile source and curated totals for the same grain, interval and currency, then explain discrepancies.
Five practical steps
- Fix interval and currency
- Compute source controls
- Compute curated controls
- Classify variance
- Route definition disputes
Primary deliverable: source-to-report reconciliation worksheet.
Release and Operate Trusted Data
A pipeline becomes a service when changes and failures affect real consumers. You will prepare a reviewed release, define useful freshness and quality signals, and practise responding to a late or suspect refresh while preserving the last trusted data interval. You will also trace fields back to authorized sources and compare performance with cost and quality evidence. The module keeps technical diagnosis separate from decisions that belong to a business, platform, security or spend owner.
11 Release Data Changes with Review and Rollback
Prepare a versioned data change with test evidence, a baseline health signal and owner, named approvals and a usable rollback point.
Five practical steps
- State the change and impact
- Collect tests, reconciliation and baseline health evidence
- Name reviewers and approver
- Set deployment and rollback
- Read back post-release behavior
Primary deliverable: data-change release and rollback checklist.
12 Monitor Freshness, Quality and Pipeline Health
Define signals that distinguish a completed job from fresh, correct data for a named consumer.
Five practical steps
- Name consumer risk
- Choose freshness and quality signals
- Set local thresholds
- Route alerts to owners
- Test a false-success case
Primary deliverable: pipeline monitoring signal specification.
13 Respond to Data Incidents and Restore Trust
Triage a late or suspect data run, communicate impact and follow local recovery authority.
Five practical steps
- Establish last trusted interval
- Describe affected consumers and separate facts from hypotheses
- Bound mitigation
- Name owner and update point
- Reconcile before resuming use
Primary deliverable: data incident and recovery record.
14 Trace Lineage and Governed Access
Trace an output field to its authorized source and owner while preserving access decisions.
Five practical steps
- Trace field to source
- Record transformation and version
- Link access decision
- Find ownership gaps
- Escalate policy questions
Primary deliverable: lineage and access traceability register.
15 Measure Performance and Cost Trade-offs
Compare query or job changes against a measured baseline without inventing savings or authority.
Five practical steps
- Define workload and baseline
- Measure time and consumption
- Check output parity
- Compare trade-offs
- Seek spend or release decision
Primary deliverable: query-performance and cost comparison.
Deliver the Product and Exercise Judgment
Engineering work is complete only when the intended consumer can interpret and accept the output. You will prepare a concise data-product handoff, define a metric interface for analysts and compare materialized and shared-data paths against a specific use. Finally, you will test claims about new platform controls and judge whether a supplied data product is ready for use. These decisions rely on measured behavior, clear ownership and known limitations rather than on a vendor announcement or a green pipeline status alone.
16 Hand Off a Data Product to Its Consumer
Deliver a dataset with definitions, freshness, quality, lineage and a named acceptance decision.
Five practical steps
- Identify consumer decision
- Summarize product grain and fields
- Report checks and freshness
- State limits and support route
- Record acceptance or rejection
Primary deliverable: consumer data-product handoff pack.
17 Specify Metrics for BI and Semantic Use
Define governed metric logic that an analyst can reproduce at the stated grain and version.
Five practical steps
- Name metric owner and use
- Write formula and grain
- Specify filters and timing
- Test a sample result
- Version and communicate definition
Primary deliverable: metric dictionary and semantic contract.
18 Choose Between Materialization and Data Sharing
Compare a copied, open-format or federated data path against freshness, rights, lineage, recovery and cost.
Five practical steps
- Define consumer and constraints
- List feasible data paths
- Compare rights, access, freshness and lineage
- Test representative queries and recovery
- Record a conditional decision
Primary deliverable: open-format and federation decision record.
19 Evaluate New Platform Controls Before Adoption
Test a new data-platform control under review, separating vendor maturity from local evidence.
Five practical steps
- State the vendor claim and maturity
- Check account applicability
- Design a small test
- Compare actual behavior and risk
- Seek owner decision
Primary deliverable: platform-change evaluation checklist.
20 Evaluate Data-Product Readiness and Recommend Next Steps
Review a supplied data product's evidence and recommend a bounded readiness action to the named decision owner.
Five practical steps
- Identify the intended decision
- Inspect supplied source and model evidence
- Check quality and last trusted interval
- Prioritize gaps and owners
- Recommend use repair or escalation to the named owner
Primary deliverable: integrated data-product readiness dossier.
Applied capstone
Deliver a trusted order-and-returns dataset.
Apply the methods that fit one retail analytics request and make the resulting data usable to its named consumer.
The situation
A retail operations analyst needs a dependable next-day view of orders, returns and net sales. A late return feed and a changed return key can affect the numbers used in a business review.
Your task
Build an authorized, analytics-ready order-and-returns dataset at one row per order line. Test the transformation, reconcile the critical measures, show freshness and quality status, and give the analyst definitions, limits and a route for acceptance.
The people behind MTF
Meet MTF faculty and the learner community.
Explore the professional backgrounds of MTF faculty and learn more about the international community studying with the Institute.
Enrollment
Enroll in Professional Certificate in Data Engineering for Business Analytics
One-time course price: €10, including applicable taxes. Payment is processed securely by Stripe. No card details are stored on the MTF Institute website.
You will receive an email with access to the course. If you have any difficulties, please write to welcome@gtf.pt.
Questions and details
Frequently asked questions
Open the sections that matter to you, including delivery format, AI-supported practice and the evidence used to design the curriculum.
Who is this data engineering course for?
The course is designed for aspiring data engineers, analytics engineers, BI data developers and analysts moving into engineering work. It begins with ordinary tables and business questions, then builds toward a tested pipeline and a consumer-ready data product.
How does the course work?
The course is online and self-paced. Four modules contain 20 applied lessons and one capstone. Each lesson explains a method, provides a bounded synthetic case and asks you to practise a work product. You can work through the course over up to one month at a pace that suits your practice.
How is AI used in the practical work?
Lessons include a prompt to draft the specific work product and a separate prompt to challenge it. You compare suggestions with the supplied facts, test calculations and record uncertainty. Use only approved tools and information, and keep access, business-definition, release and acceptance decisions with the named local owners.
What evidence supports the curriculum?
The curriculum draws on a selected set of 100 directly verified U.S. employer postings and a separate review of recent changes in analytics data platforms. The open research report is archived at DOI 10.5281/zenodo.23171923. The selected vacancy sample describes observed requirements rather than national prevalence.
What practical work will I complete?
You will practise a data contract, source and access decisions, source-to-target mapping, grain-safe SQL, intake and pipeline logic, tests, reconciliation, release and monitoring records, a metric interface and a consumer handoff. The capstone asks you to deliver one tested order-and-returns dataset.
What is data engineering for business analytics?
It is the work of turning authorized source data into reliable, understandable inputs for analysis. In this course you connect the consumer's decision to data definitions, transformations, quality evidence, refresh behavior and a clear handoff so a number can be traced and used.
What certificate and access will I receive?
After successful enrollment, you receive access to the MTF learning platform. The course includes an MTF Institute professional certificate activity and a separate Student ID activity, available within the course.