Databricks Data Engineer interview questions & guide 2026

These questions are based on real interview experiences from candidates who interviewed at this company. You can practice answering them interactively on Dataford to better prepare for your interview.

Preparing for a Data Engineering interview at Databricks requires a shift in mindset from "user" to "builder." You are expected to know not just how to use Spark or SQL, but how they work under the hood. The interview process is designed to test your raw engineering problem-solving skills alongside your domain expertise.

Key Evaluation Criteria:

Engineering Fundamentals & Coding We evaluate your ability to write clean, efficient, and robust code. While this is a data role, we expect proficiency in algorithms and data structures similar to our software engineering track. You must demonstrate that you can solve complex logical problems programmatically, not just write ETL scripts.

System Design & Architecture You will be assessed on your ability to design end-to-end data platforms. We look for candidates who can make trade-offs between batch and streaming, choose the right partitioning strategies, and design for fault tolerance and scalability within a cloud environment (AWS, Azure, or GCP).

Spark & Distributed Computing Knowledge As the creators of Spark, we expect deep familiarity with distributed computing concepts. You should understand how data is processed across a cluster, how to handle data skew, how the Catalyst optimizer works, and the nuances of the Delta Lake transaction log.

Culture Fit & Ownership Databricks values "Customer Obsession" and "First Principles" thinking. We look for candidates who take ownership of their data products, communicate clearly with stakeholders, and thrive in an environment where technical excellence is the norm.

The interview process at Databricks is rigorous and standardized to ensure fairness and high quality. It typically moves quickly, but the bar is set high. Generally, the process begins with a recruiter screen to align on your background and interests. This is followed by a technical screen, which may be conducted by a third-party provider like Karat or an internal engineer. This screen usually focuses on coding and SQL fundamentals.

If you pass the screen, you will move to the onsite loop (currently virtual). This loop comprises 4 to 5 rounds covering coding, SQL/data modeling, system design, and behavioral questions. The "Databricks difference" here is the depth of the technical questions. You won't just be asked to write a query; you'll be asked to optimize it. You won't just be asked to design a pipeline; you'll be asked how it handles failure at a specific node level.

We emphasize a "first principles" approach. Interviewers want to see you derive solutions from the ground up rather than relying on buzzwords. The process is designed to find engineers who are adaptable and can learn our proprietary technologies quickly.

This timeline illustrates the typical flow from application to offer. Note that the "Technical Screen" is a major filter; ensure your coding and SQL skills are sharp before reaching this stage. The onsite rounds are intense, so plan your energy accordingly to maintain focus through the final behavioral interview.

Based on candidate data and internal standards, the Databricks Data Engineer interview focuses on these specific technical pillars.

Coding and Algorithms

This is often the stumbling block for many data engineers who are used to SQL-heavy roles. At Databricks, you must be comfortable writing production-level code in Python or Scala.

Be ready to go over:

• Data Structures: Arrays, Hash Maps, Sets, and knowing when to use them for O(n) performance.
• Algorithms: Sliding windows, two pointers, and string manipulation.
• Data Processing Logic: Writing a function to parse a complex log file or transform a nested JSON structure without using a library like Pandas initially.

Example questions or scenarios:

• "Given a stream of log data, write a program to identify the top K frequent IP addresses in the last hour."
• "Implement a function to flatten a nested dictionary of arbitrary depth."
• "Write an algorithm to merge overlapping time intervals from a dataset."

SQL and Data Modeling

You need to demonstrate that you can model complex business logic into efficient data schemas. We look for dimensional modeling expertise tailored for the Lakehouse (Bronze/Silver/Gold architecture).

Be ready to go over:

• Complex Joins & Aggregations: Self-joins, cross-joins, and understanding the performance implications of joining large fact tables.
• Window Functions: Ranking, moving averages, and cumulative sums are fair game.
• Schema Design: Star schema vs. Snowflake schema, and specifically how to design tables for Delta Lake (partitioning, Z-ordering).

Example questions or scenarios:

• "Design a data model for a ride-sharing app. How would you track trip history and driver status changes?"
• "Write a query to find the top 3 users by spend for each month, including users with zero spend in previous months."
• "How would you handle slowly changing dimensions (SCD Type 2) in a Lakehouse architecture?"

Distributed Systems & Spark Internals

Since you are interviewing at Databricks, this is a distinguishing factor. You need to know why a job is slow, not just that it is slow.

Be ready to go over:

• Spark Architecture: Driver vs. Worker, Executors, Slots, and the DAG scheduler.
• Optimization: Broadcast joins vs. Shuffle Hash joins, handling data skew (salting), and cache/persist strategies.
• Delta Lake: How the transaction log (_delta_log) ensures ACID compliance and enables time travel.

Example questions or scenarios:

• "Your Spark job is failing with an OutOfMemory error during a shuffle. How do you debug and fix it?"
• "Explain the difference between a wide transformation and a narrow transformation in Spark."
• "How does Delta Lake handle concurrent writes to the same table?"

The word cloud above highlights the frequency of topics reported by candidates. Notice the prominence of SQL, Spark, Python, and Optimization. This signals that while general coding is required, your ability to manipulate and optimize data workloads is the core competency being tested.

As a Data Engineer at Databricks, your daily work revolves around building the "source of truth" for the company. You will design, build, and maintain high-performance data pipelines that ingest data from various sources (product telemetry, Salesforce, billing systems) into the internal Databricks Lakehouse.

You will collaborate closely with Data Scientists to productionize machine learning models. This involves setting up feature stores, managing model inference pipelines, and ensuring data quality for training sets. You are also responsible for "FinOps" within the data platform—monitoring compute usage and optimizing clusters to ensure internal costs are managed efficiently.

A significant part of the role is collaboration with the product engineering teams. You will provide feedback on new Databricks features (like Delta Live Tables or Unity Catalog) before they are released to the public. You are the first customer, and your feedback helps shape the product.

Successful candidates typically blend software engineering discipline with data engineering intuition.

• Must-have skills:

  • Proficiency in Python or Scala: You must be able to write functional, object-oriented code.
  • Advanced SQL: Ability to write complex, performant queries and understand query plans.
  • Spark/Big Data Ecosystem: Deep understanding of Apache Spark, Databricks, or similar distributed processing frameworks.
  • Cloud Infrastructure: Experience with AWS, Azure, or GCP (S3, EC2, IAM, networking).

• Nice-to-have skills:

  • Delta Lake & Lakehouse Architecture: specific experience implementing Medallion Architecture (Bronze/Silver/Gold).
  • Orchestration Tools: Experience with Databricks Workflows, Airflow, or dbt.
  • CI/CD & DevOps: Familiarity with Terraform, Jenkins, or GitHub Actions for deploying data infrastructure.

Q: How much Python vs. SQL should I expect? Expect a mix. The initial screen is often coding-heavy (Python/Scala), while onsite rounds will have dedicated slots for SQL and System Design. You cannot rely on SQL alone.

Q: Do I need to know Databricks-specific features like Delta Live Tables (DLT)? While not strictly required if you come from a different stack, knowing the concepts of the Lakehouse and Delta Lake is highly recommended. It shows you have done your research and understand the product you will be using.

Q: What is the hardest part of the interview? For most candidates, it is the "Spark Internals" or low-level optimization questions. Many engineers use Spark APIs but don't understand the underlying RDDs, shuffling mechanisms, or memory management.

Q: Is this a remote role? Databricks generally operates on a hybrid model, but this varies by team and specific job posting. Be prepared to discuss your location preferences with the recruiter early on.

• Know the Product: Read the Databricks Engineering Blog. Understanding how they solved their own technical challenges (e.g., how they built the Photon engine or Unity Catalog) gives you excellent talking points and demonstrates genuine interest.
• Think in "Distributed" Terms: When answering coding or design questions, always ask yourself: "What if the data size increases by 100x?" or "What if one node fails?" Your solutions must be scalable by default.
• Communicate Your Assumptions: In system design and SQL questions, explicitly state your assumptions about data volume, velocity, and variety. Interviewers want to see how you scope ambiguity.
• Brush up on "Data Structures for Data": Beyond standard CS structures, understand structures relevant to databases, like Bloom Filters, HyperLogLog, and LSM trees. These often come up in deep-dive discussions.

The Data Engineer role at Databricks is a career-defining opportunity to work at the cutting edge of the data industry. You are not just a consumer of technology; you are part of the ecosystem that is defining the future of data and AI. The bar is high, requiring a blend of software engineering prowess and deep data intuition.

To succeed, focus your preparation on three pillars: Coding Fluency (standard algorithms), SQL/Modeling Mastery (complex logic and schema design), and Distributed Systems Knowledge (Spark/Delta internals). Don't underestimate the behavioral component—show that you are a builder who takes ownership.

The compensation data above reflects the competitive nature of this role. Databricks typically offers top-tier packages including base salary, equity (RSUs), and bonuses, commensurate with the high technical expectations. Use this as motivation to prepare thoroughly. Good luck!