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Analytics Engineer: role, SQL, data modelling and skills

Explore the Analytics Engineer role, SQL transformations, modelling, data testing, documentation and Mandragore learning paths.

Who is this article for?

For Data Analysts moving toward industrial data transformation, SQL/BI professionals and teams clarifying the boundary between Data Engineer, Analytics Engineer and Data Analyst.

What you need to understand

Analytics Engineers work between data infrastructure and analysis, focusing on transformation and usability rather than building every ingestion component.

Core practices include maintainable SQL, dimensional modelling, data tests, documentation, Git, code review, analytics CI/CD, lineage and observability.

Why this skill matters today

When teams recreate the same KPI in separate dashboards, definitions diverge and trust declines.

Cloud warehouses and ELT workflows have strengthened this role by moving analytical transformations into version-controlled code.

What you actually do with this skill

Transform dataTurn raw sources into understandable analytical models.
Model business conceptsCreate facts, dimensions and shared metrics.
Test qualityAutomate uniqueness, completeness and relationship checks.
Document and traceExplain provenance and transformations.
Deploy changesVersion, review and release analytical code safely.

Concrete example: Standardising revenue calculation

Finance, sales and marketing use three different calculations for the same KPI.

Problem : Remove conflicting definitions.

Context : Multiple sources, discounts, cancellations and date rules.

Skills : SQL, dimensional modelling, tests, documentation, Git and metric governance.

Example : Build a certified model with one shared net-revenue definition.

Hands-on application : Test edge cases, document the metric and migrate dashboards to the common layer.

Skills to master

Professional mastery develops progressively from foundations to autonomous practice.

BeginnerSQL, relational modelling, BI, Git and data quality.
IntermediateDimensional modelling, ELT, tests, documentation and cloud warehouses.
Advanced / autonomousAnalytics CI/CD, lineage, observability, metric governance and architecture.

Errors, limitations and points of attention

  • Duplicating calculations.
  • Not testing keys and relationships.
  • Mixing business logic with ad-hoc fixes.
  • Changing metrics without downstream impact analysis.
  • Neglecting documentation and ownership.

How to progress

BeginnerSQL, relational modelling, BI, Git and data quality.
IntermediateDimensional modelling, ELT, tests, documentation and cloud warehouses.
Advanced / autonomousAnalytics CI/CD, lineage, observability, metric governance and architecture.
Mandragore Learning Academy

Train with Mandragore Learning Academy

Mandragore Learning Academy does not yet offer one professional programme named exactly “Analytics Engineer”. SQL and Data Modeling & Pipelines are directly relevant, complemented by Python, Cloud and Power BI.

The Analytics Engineer competency path organises these programmes around the job objective of producing reliable, tested and reusable analytical data.

View recommended training →
Related competency path

Related competency path

The related competency path shows essential, recommended and complementary skills around this role.

View the competency path →
Sources and references

Mandragore source: config_metiers.php remains the source of truth for the role and learning paths actually available.