How Meta interviews Data Engineers

Meta does not publish a Data Engineer specific process. What it publishes is the software engineering loop, which is the closest documented reference and the shape a data engineer should prepare against while knowing it may differ.

• Technical screen (software engineers). A 45-minute conversation with a Meta engineer, primarily a coding interview: 5 minutes of introductions, 35 minutes of coding, and 5 minutes for your questions. If the screen is not in person, the engineer sends a collaborative editing tool; in person, you use a whiteboard.
• The full loop (software engineers). A coding interview with general coding questions, a design interview, and a behavioral interview covering previous work experience and motivations. The design and behavioral interviews are each 45 minutes, and the loop coding interview follows the same 45-minute format with 35 minutes of coding.

Meta does not publish how long the process takes, how many rounds a Data Engineer has, or whether the coding is SQL rather than general programming for data roles. Screen, coding, system design, behavioral and onsite are preparation categories on this page, not Meta's round names.

The published engineering loop tests three things in three separate interviews: coding, design, and previous work plus motivations. A data engineer preparing for Meta should assume the same three lenses and fill each with data-shaped content, while remembering that this is inference from the engineering loop, not a published Data Engineer format.

Data Engineer interviews in general assess:

• SQL and data modeling. Correct, readable queries under a clock, and schemas you can defend against changing requirements. With 35 minutes of coding in a published engineering screen, speed and correctness both matter.
• Pipelines and orchestration. Dependencies, idempotent reruns, backfills, and handling late or malformed sources.
• Distributed data systems. Partitioning, storage formats, batch against streaming, and the cost of each, which is what a design interview for this role would naturally cover.
• Data quality and ownership. Detection, communication and prevention, which is the material for a behavioral interview about previous work.

Because the only published loop is the engineering one, the useful comparison is with that, and the differences below are general practice for data roles rather than anything Meta states.

• Coding. The engineering screen is primarily general coding. For a data engineer, expect the coding to lean toward SQL, data manipulation and transformation logic, but prepare general coding too, since Meta does not say which applies.
• Design. An engineering design interview is about systems; a data engineering design conversation is usually about data flow, storage, freshness and quality. Prepare to draw a pipeline end to end rather than a request-serving architecture.
• Behavioral. The published behavioral interview covers previous work experience and motivations. That is role-independent, so prepare it exactly as published: specific past work, and a clear reason you want this role.

The published durations are per interview: a 45-minute screen and 45-minute loop interviews, with 35 minutes of coding in the coding ones. Meta does not publish a timeline for any role.

The difficulty in the published format is density. With introductions and questions taking the first and last few minutes, the coding portion is a single uninterrupted block, so there is no time to warm up. For a data engineer that argues for practicing SQL and transformation problems against a clock, and for preparing design and behavioral material that can be delivered inside 45 minutes without rushing.

Mirror the three published lenses and fill them with data work.

• Timed coding. Practice SQL and data manipulation problems in 35-minute blocks in a shared editor, narrating as you go, since the published screen uses a collaborative editing tool when remote.
• Two pipeline designs. Systems you have built or would build, drawn end to end, with failure modes: late data, duplicates, schema drift, backfills that must not double count.
• Previous work and motivations. The published behavioral interview covers both; prepare specific incidents and an honest answer to why Meta and why this role.
• Quality ownership stories. A data quality problem you caught, who you told, and what you changed.
• Rehearse the format. A mock interview with SubcueAI lets you practice a 45-minute block that moves from coding to follow-up questions without a break.