Data, AI & Analytics
Data Engineering & Platforms skills assessment
Data engineers make reliable data available to everyone. This scorecard covers pipelines, modelling, cloud platforms, quality and operations for data engineering teams.
- 10 skills to score
- 1–10 scale on every skill
- 5 starting levels
The 10 Data Engineering & Platforms skills you can score
These are the default Data Engineering & Platforms skills in SkillScorer. Each one is scored from 1 to 10 for every analyst, and you can edit, remove or add skills at any time.
Pipeline Design and Orchestration
Builds reliable, maintainable pipelines with scheduling and recovery.
Data Modelling and Warehousing
Designs dimensional and lake models that are efficient and understandable.
SQL and Distributed Processing
Uses SQL and tools such as Spark to process large datasets.
Cloud Data Platform Skills
Uses cloud storage, compute and warehouse services effectively and economically.
Data Quality Testing and Monitoring
Automates tests and alerts that catch bad data early.
Streaming and Real-Time Data
Builds and operates streaming pipelines where needed.
Security, Privacy and Access Control
Protects sensitive data with proper controls and masking.
Documentation and Data Cataloguing
Documents sources, definitions and lineage.
Performance and Cost Optimisation
Tunes queries and storage to reduce time and cost.
Incident Response and Reliability
Responds to pipeline failures and reduces repeat problems.
Levels included with Data Engineering & Platforms
Data Engineering & Platforms starts with 5 levels, which SkillScorer calls “Seniority Level” for data, ai & analytics, so analysts can be grouped and compared fairly. Rename, reorder or replace them at any time.
- Junior Engineer
- Data Engineer
- Senior Engineer
- Lead
- Head of Data Engineering
How Data Engineering & Platforms works in SkillScorer
Score
Each analyst in a team is scored on the Data Engineering & Platforms skills in the Skills Matrix: tap a cell, type a score, and it saves itself.
Compare
Rank analysts, compare two side by side, or compare your teams against each other to see exactly where the strengths and gaps are.
Track
Scores are kept year after year, so you can see who is improving and which Data Engineering & Platforms skills need attention. Reports also follow each team lead's team.
Data Engineering & Platforms scorecard: common questions
What skills does the Data Engineering & Platforms scorecard include?
SkillScorer's Data Engineering & Platforms scorecard covers 10 skills: Pipeline Design and Orchestration, Data Modelling and Warehousing, SQL and Distributed Processing, Cloud Data Platform Skills, Data Quality Testing and Monitoring, Streaming and Real-Time Data, Security, Privacy and Access Control, Documentation and Data Cataloguing, Performance and Cost Optimisation and Incident Response and Reliability. Each one comes with a written description of what it measures, and you can edit, rename, remove or add skills to suit your organisation.
How are analysts scored in Data Engineering & Platforms?
Every analyst is scored from 1 to 10 on each skill (1 = Poor, 5 = Average, 10 = Elite), one team and year at a time. Scores autosave in the Skills Matrix, and the 18 built-in reports turn them into rankings, radar charts, skill gaps and year-on-year trends.
Which levels come with Data Engineering & Platforms?
Data Engineering & Platforms starts with these levels (called “Seniority Level” in the app): Junior Engineer, Data Engineer, Senior Engineer, Lead and Head of Data Engineering. They are just a starting point, so you can rename, reorder or replace them.
Can I change the Data Engineering & Platforms skills and wording?
Yes. The skills, levels and labels are all editable per organisation. In data, ai & analytics the app talks about analysts, teams and team leads, and you can change any of those words too.
Score Data Engineering & Platforms with SkillScorer
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