data quality

A query that errors is a bug. A query that runs and returns a plausible wrong number is a product teaching a finance team, one figure at a time, not to trust it. The diagnosis is a classification exercise, and the fix is almost never a better prompt.

The quintessential FDE practical: messy real-world input, grouping, ranking, and a hidden rubric line for how you treat malformed data. Most candidates parse happily and lose the round on the lines that don't parse.

Sessionization is the analytics primitive behind every 'how do users actually behave' question, and a favorite practical screen. The algorithm is easy; out-of-order events and duplicates are where the round is actually scored.

Every data migration at every customer site needs this exact tool, which is why Palantir and Retool keep asking it. The diff is a dict comparison; the grade hides in keys, duplicates, and column drift.

Palantir's bread and butter: 'Acme Corp.' and 'ACME Corporation, Inc.' are the same customer; prove it in code. Normalization does more work than the clever algorithm, and blocking is what makes it run before the heat death of the universe.

Profile before you model: shape, types, target balance, leakage check. Then the part most candidates botch, that a missing value is often the most predictive feature in the table, so you flag it before you fill it. The first-hour playbook and the imputation decisions that survive production.

Thirty percent disagreement is not a fact about the annotators. It is a fact about the task definition, and on a skewed label set it can mean the labels are worse than chance. The week is spent on the guideline and the gold set, not on the model, because no model trains past the noise in its labels.

Pure FDE territory: the customer's data is always dirtier than scoped. The layered defense, contracts, quarantine, drift handling, and the alert-severity matrix that keeps you from crying wolf.

The most common escalation in any data deployment, and the one that quietly ends trust in the platform. Four causes produce it, one of them means nobody is wrong, and you cannot tell them apart without running the ladder in order.

Everyone writes the join and the ratio. Then the result comes back dominated by people who bought one thing and sent it back, and the list is useless to the person who asked for it.

Every enterprise AI deployment hits this wall, interviewers want to know if you deliver the bad news early, with options, or sit on it hoping. Here's the conversation script, beat by beat.