How to Scrape LinkedIn Profile Skills (2026 Guide)

Why Skills-Based Sourcing Outperforms Job Titles

Job titles across technology, finance, and marketing are notoriously inconsistent. One company's 'Senior Growth Analyst' is another company's 'Product Manager'.

By scraping and filtering candidate skills (e.g. 'Postgres', 'dbt', 'HubSpot Admin', 'Outbound Sales'), talent teams and B2B marketers evaluate prospects based on verifiable operational capabilities rather than inflated titles.

Skills are also more stable than titles. A candidate's title changes with every reorg; their core skills persist across roles. This makes skills a more reliable long-term targeting dimension for saved searches and recurring campaigns.

Extracting and Normalizing Skills Data

Raw skill exports contain inconsistencies. The same capability appears as 'postgres', 'PostgreSQL', and 'Postgres DB'. A normalization layer maps all variants to a canonical taxonomy so CRM filters and reporting work cleanly.

Normalization is not optional. Without it, your CRM ends up with 12 separate fields for what is essentially one technology, and every report requires manual cleanup before it is trustworthy.

• •Map skill aliases to canonical names (postgres, PostgreSQL -> PostgreSQL).
• •Track endorsement counts as a proxy for expertise depth.
• •Separate 'featured' pinned skills from general endorsements.
• •Flag skills with fewer than 3 endorsements as low-confidence.