Finished the MongoDB data engineer interview loop last month for a role on their internal data platform team. Sharing the full breakdown because there's almost nothing out there on the DE-specific process.
First thing to know: this is a genuinely different loop from the DS or SWE track. It has overlap but the emphasis is different.
Phone screen with HM: 30 minutes. They cared about two things: have I actually built and operated pipelines (not just used dbt or Spark in a data science workflow), and do I understand MongoDB's data model. You don't need to be a MongoDB expert, but knowing the document model vs. relational differences, and knowing what Atlas is, is table stakes.
Technical round 1: SQL and pipeline architecture. 60 minutes. Heavy SQL: complex CTEs, window functions, incremental load patterns. One question was specifically about building an incremental pipeline on top of MongoDB change streams, which is obviously in their wheelhouse. I had to sketch out how I'd design a bronze/silver/gold Medallion architecture on top of Atlas. Know that concept.
Technical round 2: Live coding. Python focus. They gave me a data transformation problem: take a nested JSON structure (document model!), flatten it according to some rules, and handle edge cases. Not super hard but watch out for the edge cases. They explicitly said they care about code quality and error handling, not just correctness.
Systems design for DE: More of a data architecture discussion than a traditional systems design round. We talked about a scenario: "You need to build a reporting layer that serves both real-time dashboards and daily batch jobs off the same MongoDB Atlas source. How do you approach this?" The CQRS pattern came up, also materialized views and read replicas. Know the trade-offs between streaming and batch.
Behavioral: One round, 45 minutes. Focused on data quality incidents, stakeholder trust, and cross-team collaboration. Standard STAR.
Comp for mid-level DE offer (NYC, 2026): base $155k, RSUs vesting over 4 years, bonus around 12% target. Didn't end up taking it (leveling mismatch), but the pay was fair for the level.
If you're applying as a DE: really lean into the document model angle. Show you understand why building pipelines on MongoDB is different from Postgres or Snowflake, and what that means for schema evolution, array handling, and nested document transformations.