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Data & AI• Sourcing Cheat Sheet
Boolean Search Strings for Data Engineer (LinkedIn, X-Ray & Dice)
Data Engineers build and optimize scalable data pipelines, extract-transform-load (ETL/ELT) workflows, and lakehouse architectures for analytics and machine learning workloads. Use the pre-tested Boolean queries below to instantly source active and passive talent across LinkedIn, Google X-Ray, Dice, and GitHub.
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("Data Engineer" OR "Big Data Engineer" OR "Data Platform Engineer") AND (Python OR Scala) AND SQL AND (Spark OR PySpark OR Databricks) AND (Snowflake OR BigQuery OR Redshift) AND (Airflow OR dbt OR Kafka)
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Generate from Full JDCore Must-Have Skills
PythonSQLPySpark / Apache SparkSnowflake / BigQuery / RedshiftApache Airflow
These keywords form the mandatory `AND` clauses of the boolean strings.
High-Signal Secondary Skills
dbt (data build tool)KafkaDatabricksAWS / GCP / AzureData Modeling
Use these in optional `OR` blocks to narrow search results when volume is too broad.
Recruiter Sourcing Insights for Data Engineer (Python & Spark / Snowflake)
- 1Modern data stacks heavily emphasize "dbt" and "Snowflake/Databricks" over legacy Hadoop/MapReduce ecosystems.
- 2Check if the role requires real-time streaming (Kafka, Flink, Spark Streaming) vs batch ETL (Airflow, Cron, SQL pipelines).
- 3Look for candidates who mention dimensional modeling (Star schema, Kimball) for enterprise analytics reqs.
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