Boolean search string for Data Engineer
Copy-paste strings to source Data Engineer candidates, plus the title synonyms, skills and exclusions they are built from. Adapt the terms to your market before running them.
LinkedIn search
("data engineer" OR "analytics engineer" OR "etl developer" OR "big data engineer" OR "data platform engineer" OR "data warehouse engineer") AND "data engineering" AND sql AND etl AND ("data pipeline" OR python OR "data warehouse" OR "data modeling" OR airflow OR spark OR dbt OR snowflake OR kafka OR elt OR streaming OR "data quality" OR lakehouse OR cdc) NOT (intern OR student OR "staffing agency" OR "data entry")Paste into the LinkedIn people-search box. LinkedIn supports AND, OR, NOT and quoted phrases.
Google X-ray
site:linkedin.com/in ("data engineer" OR "analytics engineer" OR "etl developer" OR "big data engineer" OR "data platform engineer" OR "data warehouse engineer") "data engineering" sql etl ("data pipeline" OR python OR "data warehouse" OR "data modeling" OR airflow OR spark OR dbt OR snowflake OR kafka OR elt OR streaming OR "data quality" OR lakehouse OR cdc) -intern -student -"staffing agency" -"data entry"Paste into Google. It searches public LinkedIn profiles without LinkedIn Recruiter — add a location in quotes to narrow it.
Terms behind the Data Engineer string
| Job titles | data engineer · analytics engineer · etl developer · big data engineer · data platform engineer · data warehouse engineer |
|---|---|
| Core skills | data engineering · sql · etl · data pipeline · python · data warehouse · data modeling · airflow · spark · dbt · snowflake · kafka |
| Nice to have | elt · streaming · data quality · lakehouse · cdc |
| Excluded | intern · student · staffing agency · data entry |
Sourcing notes
- Start broad, then narrow. If a string returns under ~20 profiles, drop the nice-to-have group before you drop titles.
- Exclusions cut noise but also cut real candidates — check a few excluded profiles before trusting a NOT clause.
- Titles vary by company size: what reads as senior at a startup can be mid-level at an enterprise. Read the profile, not the label.
Build a custom Boolean string → · Interview scorecard for a Data Engineer →
Frequently asked questions
What should a data engineer interview scorecard include?
Weighted competencies that reflect what breaks in production: pipeline reliability and recovery, data modeling for consumers, quality checks and contracts, schema evolution, processing cost, and orchestration. Weight reliability and modeling highest, because a pipeline that fails silently costs more than one that is slow. Anchors keep the score attached to what a candidate actually built rather than the stack they can name.
What is the difference between a data engineer and a data analyst in hiring?
A data engineer is accountable for the data arriving correct, on time and at a known cost; an analyst is accountable for what the data means. They fail differently — a bad pipeline delivers wrong numbers everywhere, a bad analysis misleads one decision. Score them on different competencies, and be explicit about which failure you are hiring against.
How do you evaluate a data engineer without a take-home project?
Ask what went wrong and what it cost. A failed pipeline and how consumers found out, a backfill that risked double counting, a job they made cheaper with the before-and-after number. Engineers who ran production systems answer with specifics; those who only built things in a course move to describing tools instead.
When the stakes are a real hire, use evidence
These tools are quick heuristics. Verdict reads the CV against your job description and scores six dimensions with verbatim quotes as evidence — a hiring document you can defend.