Company Interview Guide
Databricks created Apache Spark and Delta Lake, and they expect candidates to know both deeply. Their DE interviews go beyond API familiarity into Spark internals, lakehouse architecture, and data governance with Unity Catalog. Here is what each round tests and how to prepare.
Three stages from recruiter call to offer.
Initial call covering your background and interest in Databricks. The recruiter evaluates your experience with Spark, data lakes, and lakehouse architectures. Databricks built the lakehouse category, so they expect candidates to have strong opinions about data architecture. They also probe for your understanding of why Delta Lake exists and what problems it solves.
A coding exercise focused on Spark or SQL, often both. Databricks phone screens go deeper on Spark internals than most companies. Expect questions about optimization: why a query plan looks a certain way, how to fix a skewed shuffle, or how Delta Lake handles concurrent writes. The interviewer tests whether you understand distributed processing, not just API calls.
Four to five rounds covering system design, Spark deep dive, SQL, coding, and a behavioral round. System design at Databricks involves lakehouse architectures, data governance with Unity Catalog, and MLOps pipelines. The Spark deep dive is the most differentiating round: expect questions about query plans, memory management, and performance tuning at a level most companies do not test.
Real question types from each round. The guidance shows what the interviewer looks for.
Broadcast the 100 MB table to avoid shuffle. Check if the Delta table is Z-ordered on the join key. Look at the query plan for unnecessary full scans. Discuss partition pruning with Delta statistics and how broadcast threshold configuration works.
Catalyst parses to logical plan, optimizes (push down filters, column pruning), generates physical plan with HashAggregate. Partial aggregation happens map-side, then shuffle by key, then final aggregation. Discuss data serialization between Python and JVM.
Use table_changes() function with version range. Filter to the latest version per primary key using ROW_NUMBER. Discuss how Delta CDC works: insert, update_preimage, update_postimage, delete operations.
Check partition pruning (is the WHERE clause aligned with partitions?). Consider Z-ordering on filter columns. Add a materialized view or aggregate table for dashboard queries. Discuss file compaction (OPTIMIZE) and statistics collection (ANALYZE TABLE).
Bronze: raw ingestion with schema-on-read. Silver: cleaned, deduplicated, standardized types. Gold: business-level aggregates and feature tables. Discuss Delta Live Tables for declarative pipelines, data quality expectations at each layer, and how Unity Catalog provides governance across layers.
Batch features computed via Spark, stored in a feature store (Databricks Feature Store). Real-time features served from an online store. Discuss point-in-time correctness for training data, feature drift monitoring, and how Unity Catalog tracks feature lineage.
Bronze: raw transaction events. Silver: deduplicated transactions with standardized schemas. Gold: customer aggregate tables (lifetime value, purchase frequency, recency). Discuss how to serve the same underlying data for SQL dashboards and ML feature pipelines without duplicating storage.
Databricks is a technology company selling architectural change. Show you can articulate technical benefits clearly, address concerns about migration risk, and support adoption with documentation and enablement. Quantify the outcome.
What makes Databricks different from other companies.
Databricks created Spark. Interview questions go deeper than 'use broadcast join.' Know the Catalyst optimizer, Tungsten memory management, adaptive query execution, and how to read Spark UI DAGs. This is the single biggest differentiator.
Understand Delta Lake deeply: the transaction log (_delta_log), ACID semantics, time travel, Z-ordering, OPTIMIZE/VACUUM, and change data feed. Know how Delta differs from Iceberg and Hudi and why Databricks chose this approach.
Unity Catalog is Databricks' answer to data governance: centralized access control, lineage tracking, and audit logging across all data assets. Understand its role in the lakehouse architecture and how it enables data mesh patterns.
Databricks believes the lakehouse replaces both data warehouses and data lakes. Understand the thesis: open formats, unified batch and streaming, SQL and ML on the same data, and governance as a first-class feature. Be ready to discuss tradeoffs honestly.
Databricks DE interviews test Spark internals and lakehouse architecture at a depth most companies do not reach. Prepare accordingly.
Practice Databricks-Level SQL