Analytics Engineering
Analytics Engineering is an individual-contributor Data + AI career progressing from guided delivery to enterprise expertise without requiring people management.
- Función
- Data & Analytics
- Arquetipo
- Engineering
- Nivel más alto
- L6 · Principal Analytics Engineer
Para qué existe
Transforms modeled data into governed analytical datasets, semantic models and trusted metrics for self-service analytics.
Responsabilidades típicas
Deliver analytics engineering outcomes; apply data/AI standards; manage quality, reliability and risk; collaborate across product, engineering, security, legal and business teams; at senior levels shape enterprise standards and strategy.
Dónde se trabaja
Technology companies, data/cloud platforms, financial services, consulting, shared services, product engineering and regulated enterprises adopting Data + AI.
Cómo avanza la carrera
Associate Analytics Engineer → Analytics Engineer → Senior Analytics Engineer → Staff Analytics Engineer → Senior Staff Analytics Engineer → Principal Analytics Engineer
Niveles de esta carrera
Seis etapas estándar. La séptima existe solo donde la carrera la contempla.
Qué se espera en L2 · Analytics Engineer
Apply analytics engineering capability at independently delivers standard work within a team or defined domain.
Las cinco dimensiones que cambian
- Autonomía
- Independent routine ownership
- Alcance
- Independently delivers standard work within a team or defined domain.
- Complejidad
- Moderate
- Influencia
- Immediate team
- Impacto en el negocio
- Task/team
- Ambigüedad
- Moderate
Cómo se ve un buen desempeño
Produces trustworthy analytics engineering outcomes, makes sound trade-offs, communicates evidence and risks, and demonstrates the autonomy and impact expected at L2.
Evidencia típica
Completed analytics engineering work products or production outcomes; documented decisions; measurable improvements in quality, reliability, risk, speed, cost or business outcomes; peer/stakeholder evidence of L2 scope.
Habilidades esperadas en L2
Agrupadas en humanas, profesionales y técnicas. La proficiencia meta usa la escala P1–P7, y cada fila dice qué significa ese nivel para esa habilidad.
Humanas8
| Humanas | P | De apoyo |
|---|---|---|
| Clear verbal communicationCommunication & Language | P2FoundationalDe apoyo | Applies Clear verbal communication to routine situations using established methods and controls. |
| Clear written communicationCommunication & Language | P2FoundationalDe apoyo | Applies Clear written communication to routine situations using established methods and controls. |
| CollaborationCollaboration & Relationships | P2FoundationalDe apoyo | Applies Collaboration to routine situations using established methods and controls. |
| Critical thinkingThinking & Problem Solving | P2FoundationalDe apoyo | Applies Critical thinking to routine situations using established methods and controls. |
| Cross-functional collaborationCollaboration & Relationships | P2FoundationalDe apoyo | Applies Cross-functional collaboration to routine situations using established methods and controls. |
| Learning agilityExecution & Self-Management | P2FoundationalDe apoyo | Applies Learning agility to routine situations using established methods and controls. |
| Stakeholder alignmentInfluence & Leadership Without Authority | P2FoundationalDe apoyo | Applies Stakeholder alignment to routine situations using established methods and controls. |
| Structured problem solvingThinking & Problem Solving | P2FoundationalDe apoyo | Applies Structured problem solving to routine situations using established methods and controls. |
Profesionales7
| Profesionales | P | De apoyo |
|---|---|---|
| Analytics problem framingData & Analytics Practice | P2FoundationalCentral | Applies Analytics problem framing to routine situations using established methods and controls. |
| Data contracts governanceData & Analytics Practice | P2FoundationalDe apoyo | Applies Data contracts governance to routine situations using established methods and controls. |
| Data governanceCybersecurity, Privacy & Data Governance Standards | P2FoundationalDe apoyo | Applies Data governance to routine situations using established methods and controls. |
| Data product managementData & Analytics Practice | P2FoundationalDe apoyo | Applies Data product management to routine situations using established methods and controls. |
| Data quality managementCybersecurity, Privacy & Data Governance Standards | P2FoundationalDe apoyo | Applies Data quality management to routine situations using established methods and controls. |
| KPI designData & Analytics Practice | P2FoundationalCentral | Applies KPI design to routine situations using established methods and controls. |
| Metrics definitionData & Analytics Practice | P2FoundationalCentral | Applies Metrics definition to routine situations using established methods and controls. |
Técnicas10
| Técnicas | P | De apoyo |
|---|---|---|
| Dashboard developmentAnalytics, BI & Data Visualization | P2FoundationalDe apoyo | Uses Dashboard development for routine tasks with documented patterns and review. |
| Data catalog platformsDatabases & Data Platforms | P2FoundationalDe apoyo | Uses Data catalog platforms for routine tasks with documented patterns and review. |
| Data contract implementationDatabases & Data Platforms | P2FoundationalDe apoyo | Uses Data contract implementation for routine tasks with documented patterns and review. |
| Data lineage toolingDatabases & Data Platforms | P2FoundationalDe apoyo | Uses Data lineage tooling for routine tasks with documented patterns and review. |
| Data modelingDatabases & Data Platforms | P2FoundationalCentral | Uses Data modeling for routine tasks with documented patterns and review. |
| Data visualization implementationAnalytics, BI & Data Visualization | P2FoundationalDe apoyo | Uses Data visualization implementation for routine tasks with documented patterns and review. |
| Dimensional modelingDatabases & Data Platforms | P2FoundationalCentral | Uses Dimensional modeling for routine tasks with documented patterns and review. |
| SQL analyticsAnalytics, BI & Data Visualization | P2FoundationalCentral | Uses SQL analytics for routine tasks with documented patterns and review. |
| Semantic layer engineeringAnalytics, BI & Data Visualization | P2FoundationalCentral | Uses Semantic layer engineering for routine tasks with documented patterns and review. |
| dbtDatabases & Data Platforms | P2FoundationalCentral | Uses dbt for routine tasks with documented patterns and review. |
Qué cambia al pasar de L2 a L3
Moving from Analytics Engineer to Senior Analytics Engineer means greater autonomy, complexity, scope, influence and evidence of impact—not simply more tools.
Cómo se demuestra que ya estás
Sustained evidence of operating at L3-type scope: handles representative complexity, influences expected stakeholders and produces durable measurable outcomes.
Cómo prepararse
Take on one assignment at next-level scope; seek feedback; document decisions/outcomes; mentor others where appropriate; close highest-priority skill gaps.
Carreras cercanas
Calculadas por habilidades compartidas. Es una señal para explorar, no una garantía de contratación ni de elegibilidad.
- Data ArchitectureData & Analytics21 habilidades compartidas
- AI Governance & Responsible AIData & Analytics12 habilidades compartidas
- AI Platform & MLOps EngineeringData & Analytics12 habilidades compartidas
- Data AnalyticsData & Analytics15 habilidades compartidas
- Business IntelligenceData & Analytics15 habilidades compartidas
- Data EngineeringData & Analytics15 habilidades compartidas
Marco de carreras v21, activo desde 17 de agosto de 2026.