EAB Group Data Engineer Interview Guide

SQL carries the most weight in this loop. The screen focuses on SQL, and the full loop gets into pipeline architecture, which at EAB Group means you should expect questions about structuring ETL for multi-tenant data where schemas vary by institution. A strong answer here goes beyond query correctness: it accounts for how you would handle a client whose enrollment table has different column names than the 12 clients before them. The stack is AWS and Pandas with Python and SQL, so Python proficiency around data transformation is assumed. Design questions will likely center on batch pipelines and data modeling for reporting, not streaming. What distinguishes a passing answer at EAB from a passing answer elsewhere is the education-domain context: you should be able to speak to slowly changing dimensions, reporting period logic, and how you'd model something like a student cohort across time.

The failure mode that comes up repeatedly in loops like this one is over-engineering. Candidates who arrive thinking about Flink, distributed compute, or a full lakehouse rebuild tend to lose the room quickly, because the actual infrastructure is Pandas on AWS and the team is not looking to change that in the near term. Strong candidates read the operational reality and design within it, proposing something that a small team can maintain rather than something that would require 3 more engineers to run. The inverse failure is under-specification: candidates who write SQL that works for 1 client but breaks silently when client 14 sends a slightly different schema. The hire signal is a candidate who defaults to parameterized, testable pipeline logic and can explain the tradeoffs of that approach to someone without a data engineering background.

EAB Group's interview loop is filtering for engineers who can work with ambiguity at the data source, not at the product level. The company's core business is helping colleges and universities make better decisions about enrollment, retention, and student outcomes. That means the data coming in is institution-specific, inconsistently formatted, and owned by clients who have little incentive to clean it up before it arrives. The loop is designed to surface candidates who can take that kind of raw, non-standard input and produce reliable outputs that advisors actually trust. What gets noticed is not speed or architectural sophistication; it's the instinct to ask what the downstream consumer needs before designing the pipeline, and the discipline to build something that holds up when the next client sends data in a completely different shape.