Spark Data Engineer Jobs

US data engineer openings whose parsed job description names Apache Spark. About 1,000 active listings, the most-tagged big-data processing engine in the catalog. PySpark, Databricks, Delta Lake, and EMR are the dominant supporting tags; about 80 percent of Spark-tagged listings also tag Databricks or AWS EMR as the runtime.

Spark Data Engineer Jobs

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

Frequently asked questions

How many Apache Spark data engineer jobs are listed at once?

Around 1,000 active listings name Spark in the parsed JD. Spark is the most-tagged big-data processing tool in the catalog, ahead of Databricks (which uses Spark under the hood) by about 200 listings because some companies run Spark on EMR, Dataproc, or self-managed clusters rather than Databricks.

What Spark-specific topics come up in these data engineer interviews?

Five recur. Skew handling (salting hot keys, broadcast hash joins when the small side fits). Adaptive Query Execution and what AQE can and cannot fix automatically. Cache eviction in iterative ML loops (the unpersist trap). Catalyst's predicate pushdown and when filters do not push down. Reading the Spark UI to find the stage that dominates total runtime.

Do I need to know Scala Spark, or is PySpark enough for these roles?

PySpark is enough at about 85 percent of Spark-tagged listings in the catalog. Scala still appears at companies with legacy pipelines (older banks, ad-tech, some game studios) and at companies that contribute upstream to Spark. New work is overwhelmingly PySpark; if you target the modal catalog listing, PySpark plus deep understanding of the execution model wins.

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

Three common shapes. Databricks: S3 or ADLS plus Spark plus Delta Lake. EMR: S3 plus Spark plus Hive metastore. Self-managed: HDFS or S3 plus Spark plus Iceberg or Hudi for table format. The Databricks and EMR shapes dominate; self-managed Spark is declining as the per-cluster operations overhead pushes companies toward managed offerings.

How does Spark interview prep differ from generic data engineer prep?

Two differences. First, system design rounds at Spark-heavy companies expect you to design pipelines with explicit shuffle, partition, and skew thinking, not just dataflow boxes. Second, coding rounds often skip the SQL warmup and go straight to a Spark transformation question (write the DataFrame chain to compute X) where the grader reads your shuffle and broadcast choices.

What pay range should I expect for Spark-heavy data engineer roles?

Slightly above the catalog median. Spark-tagged roles at large-cap companies (Meta, Netflix, Stripe, Databricks itself) sit at the top of the comp range. Senior Spark DE roles typically cluster in the $180K to $260K base range with equity adding 30 to 80 percent on top. Spark expertise is harder to fake than SQL expertise, so the comp premium is durable.

Canonical URL: https://datadriven.io/spark-data-engineer-jobs