Data Engineering at Ernst & Young LLP

The employer trade at EY is real but uneven. Pay at L4 comes in slightly above relative to the other companies we track, and with 28 reports in the pool the signal is directionally credible. The Glassdoor rating sits at 3.7, a little below the middle of the pack, which is consistent with what consulting firms generally produce: structured career ladders and brand recognition on one side, client-driven hours and limited ownership on the other. Sentiment is neutral and roughly flat, which suggests engineers are neither actively leaving nor particularly energized. The tension is predictable for this category: EY's name opens doors for your next move, but the work itself is often defined by the client's constraints and timeline, and internal tooling investment tends to lag what a tech-native employer would budget.

Ernst & Young LLP's data engineering work is shaped by the consulting model itself: pipelines don't serve one internal product, they serve dozens of client engagements across financial services, healthcare, government, and manufacturing. The core data problems shift by engagement, which means you're less likely to own a single warehouse for years and more likely to build, hand off, and move on. The visible stack, Azure, GCP and AWS alongside SQL, Python and Scala, reflects client environments rather than a house architecture; EY's engineers are expected to be portable across cloud vendors and adapt to whatever the client already runs. That makes the day-to-day closer to a series of short-cycle delivery projects than to the long-horizon platform work you'd find at a product company. If you want to go deep on one system, this isn't structured for that.