Databricks Data Engineer Jobs

US data engineer openings whose parsed job description names Databricks as the processing or lakehouse platform. About 825 active listings, weighted toward enterprise and large-cap companies running Spark at scale plus the growth-stage cohort adopting Delta Lake and Unity Catalog. Spark, Delta Lake, and MLflow are the dominant supporting tags.

Databricks Data Engineer Jobs

US data engineer listings that name Databricks in the posting, scored against your profile with salary and layoff signals.

Frequently asked questions

How many Databricks data engineer jobs are listed at once?

Around 825 active listings name Databricks in the parsed JD. The set leans large-cap and enterprise (banks, retailers, large healthcare) plus growth-stage companies on the Delta Lake plus Unity Catalog migration. Many listings also tag Spark (about 1,035 listings tag Spark; the overlap with Databricks is large).

Do I need PySpark experience for Databricks data engineer roles?

Usually yes. About 60 to 70 percent of Databricks-tagged listings name PySpark or Spark in the requirements. Scala-based Spark still appears at companies with legacy pipelines but is declining; PySpark is the default for new work. SparkSQL appears as a fallback for engineers more comfortable in SQL than in Python.

What Databricks-specific concepts come up in these data engineer interviews?

Delta Lake mechanics (transaction log, time travel, OPTIMIZE and VACUUM, MERGE INTO with WHEN MATCHED). Photon vs JVM execution and when Photon falls back. Unity Catalog and the move from workspace-scoped to account-scoped governance. Job clusters vs all-purpose clusters and the cost implications. Spark UI reading (stages, tasks, skew detection).

What's the typical Databricks stack for the data engineer jobs in this catalog?

Cloud storage (S3, ADLS, GCS) plus Databricks workspace plus Delta Lake plus Unity Catalog. Workflows or Airflow for orchestration; native Databricks Workflows is gaining share. MLflow for ML pipeline tracking when the role spans ML engineering. Power BI (about 90 percent of Azure-Databricks listings) or Tableau or Looker for the BI layer.

How do Databricks and Snowflake compare in the catalog?

Databricks shows up in about 825 listings, Snowflake in about 950. Companies running heavy Spark workloads or ML pipelines tend to land on Databricks; companies with predominantly SQL-driven analytics tend to land on Snowflake. About 18 percent of listings tag both, usually for Snowflake-as-warehouse plus Databricks-as-processing architectures.

Is the Databricks Certified Data Engineer cert worth pursuing?

Useful for ATS keyword screens, especially at Databricks consulting partners (DataBricks Partner Connect agencies). The Associate cert is the practical floor; the Professional cert signals senior-level depth. For hands-on interviews, a portfolio project on Databricks Community Edition (read CSVs, build a Delta Lake bronze-silver-gold pipeline, schedule with Workflows) outweighs the cert.

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