For a Databricks engineering interview, prepare for two things at once: ordinary coding and algorithm questions, and a working understanding of how distributed data systems behave. Which one dominates depends on the role. A platform or infrastructure engineer will be tested mostly on coding and system design. A data engineering, solutions or ML role will also be asked about Spark, SQL and pipelines. This page covers both and shows runnable examples for the parts you can practice on a laptop.<\/p>\n
Databricks does not publish a detailed engineering interview script, so this comes from candidate reports on Glassdoor and similar sites: a recruiter call, a coding or skills assessment, one or two technical screens, then a set of onsite rounds that mix coding, design and behavioral questions. Reports on the length of the process vary widely. Confirm the exact format with your recruiter, since it changes by team and level.<\/p>\n
Databricks was founded by the original creators of Apache Spark, and it is also behind Delta Lake and MLflow. Its pitch is the “lakehouse”: data stored as open files in cloud object storage (Parquet, with Delta Lake adding transactions on top), queried with SQL and Spark, with governance and machine learning on the same data. You do not need to have used the product to interview for most software roles, but you should be able to explain these ideas:<\/p>\n
If the role touches data processing, expect questions on how Spark actually runs a job. The ones worth knowing well:<\/p>\n
filter<\/code> and select<\/code> are lazy and build a plan. count<\/code> and write<\/code> trigger execution.<\/li>\n- Narrow versus wide transformations.<\/strong> A narrow one (map, filter) works on each partition alone. A wide one (join, groupBy) needs a shuffle: data moves across the network so that matching keys land together. Shuffles are the usual reason a job is slow.<\/li>\n
- DataFrames over RDDs.<\/strong> DataFrame and SQL queries go through the Catalyst optimizer, which can reorder filters, prune columns and pick join strategies. Hand-written RDD code gets none of that.<\/li>\n
- Join strategies.<\/strong> A broadcast join sends the small table to every executor and avoids shuffling the big one. A sort-merge join shuffles both sides. Know when each is chosen and how to hint it.<\/li>\n
- Data skew.<\/strong> If one key holds most of the rows, one task does most of the work while the rest sit idle.<\/li>\n
- Caching,<\/strong> partition sizing, and why many tiny files hurt read performance.<\/li>\n<\/ul>\n
Seeing skew and the salting fix<\/h3>\n
You can reproduce the problem without a cluster. Hash-partition 10,000 events into 4 partitions when 9,000 of them share one key, then “salt” the hot key by adding a suffix so it spreads over several partitions:<\/p>\n
import zlib\nfrom collections import Counter\n\ndef part(key, n):\n return zlib.crc32(key.encode()) % n\n\nevents = [\"hot\"] * 9000 + [f\"user{i}\" for i in range(1000)]\nn = 4\nplain = Counter(part(k, n) for k in events)\nsalted = Counter(part(k if k != \"hot\" else f\"hot#{i % 8}\", n)\n for i, k in enumerate(events))\nprint(sorted(plain.values())) # [249, 249, 251, 9251]\nprint(sorted(salted.values())) # [2499, 2499, 2501, 2501]\n<\/code><\/pre>\nWithout salting, one partition gets about 9,250 rows and the others about 250. With salting the load is even. The cost: an aggregation now needs a second step to combine the partial results for “hot#0” through “hot#7”, and a join needs the other side replicated across the salt values. Being able to state that cost is what interviewers look for. Newer Spark versions can also split skewed partitions automatically in some joins, so mention that you would check what the engine already does before hand-rolling a fix.<\/p>\n
SQL, which is asked a lot<\/h2>\n
Window functions are the most common “harder” SQL topic. Practice running totals, ranking within a group, and “top row per group”. This runs as-is in SQLite 3.25 or later, and the syntax is the same in Spark SQL:<\/p>\n
import sqlite3\n\ncon = sqlite3.connect(\":memory:\")\ncon.executescript(\"\"\"\ncreate table orders(customer text, day text, amount int);\ninsert into orders values\n ('ann','2026-01-01',50),('ann','2026-01-02',70),('ann','2026-01-03',20),\n ('bob','2026-01-01',90),('bob','2026-01-03',10);\n\"\"\")\n\nprint(con.execute(\"\"\"\nselect customer, day, amount,\n sum(amount) over (partition by customer order by day) as running_total\nfrom orders order by customer, day\n\"\"\").fetchall())\n\nprint(con.execute(\"\"\"\nselect customer, day, amount from (\n select *, row_number() over (partition by customer order by amount desc) rn\n from orders\n) where rn = 1 order by customer\n\"\"\").fetchall())\n<\/code><\/pre>\nThe first query prints running totals per customer: ann gets 50, 120, 140 and bob gets 90, 100. The second prints each customer’s largest order: ('ann', '2026-01-02', 70)<\/code> and ('bob', '2026-01-01', 90)<\/code>. Know the difference between row_number<\/code>, rank<\/code> and dense_rank<\/code> when there are ties, and be ready to explain why a correlated subquery is usually slower than a window.<\/p>\nOther topics: joins and their NULL behavior, GROUP BY<\/code> with HAVING<\/code>, deduplication, star schema versus normalized models, and reading a query plan.<\/p>\nCoding and algorithms<\/h2>\n
Software engineering loops include standard problems in a language of your choice. Python, Java, Scala and C++ are all common choices; use the one you write most fluently. Cover arrays and strings, hash maps, linked lists, trees, graphs, heaps, sliding window, binary search, and basic dynamic programming. Practice explaining time and space complexity for each solution, and write tests for edge cases (empty input, duplicates, a single element).<\/p>\n
Several good practice sets: the Hash Map<\/a> lesson, the Last Stone Weight<\/a> heap problem, and Level Order Traversal<\/a> for trees. Problems that look like data tasks (merge sorted streams, top-k per key, dedupe with limited memory) are a natural fit for this company.<\/p>\nSystem design for data systems<\/h2>\n
Typical prompts: a batch and streaming pipeline for clickstream data, a job scheduler, a metadata or catalog service, a multi-tenant query service, or “design a simplified Spark”. A sequence that works:<\/p>\n
\n- Pin down scale and freshness: events per second, data volume, latency target, how late data can arrive.<\/li>\n
- Choose batch, streaming or both, and say why.<\/li>\n
- Describe storage (object storage plus a table format), partitioning and file sizes.<\/li>\n
- Cover failure: retries must be idempotent, so a replayed task does not write duplicates. Checkpoints, exactly-once versus at-least-once, and what happens when a worker dies mid-task.<\/li>\n
- Cover cost and multi-tenancy: noisy neighbors, quotas, scaling up and down.<\/li>\n<\/ol>\n
The book Designing Data-Intensive Applications<\/em> by Martin Kleppmann is the standard reference for replication, partitioning, consistency and stream processing. Our posts on system design concepts<\/a> and questions to ask during the interview<\/a> help with structure.<\/p>\nMachine learning questions<\/h2>\n
For ML roles, expect supervised versus unsupervised learning, bias and variance, regularization, evaluation metrics for imbalanced data, feature engineering, cross-validation, and how you would deploy and monitor a model. For software roles, a general idea of the ML lifecycle (data, training, evaluation, serving, monitoring) is enough.<\/p>\n
Behavioral questions<\/h2>\n
Prepare stories about a technical problem you solved, a disagreement you resolved, a time you learned an unfamiliar system quickly, and an outcome that went wrong. Use the Situation, Task, Action, Result structure, and quantify results only with numbers you actually have.<\/p>\n
A study plan<\/h2>\n\n- Weeks 1 and 2:<\/strong> algorithms, 3 to 4 problems a day, explained aloud.<\/li>\n
- Week 2 and 3:<\/strong> SQL window functions and joins; if your role needs Spark, practice on Databricks Free Edition (it replaced Community Edition in 2025; it runs on serverless compute with usage quotas, so check the current limits in the Databricks docs) or on a local Spark install.<\/li>\n
- Week 3 and 4:<\/strong> two or three system design practice sessions with another person.<\/li>\n
- Final days:<\/strong> mock interviews, behavioral stories, questions for the interviewers.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"
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