Lyft data engineering interview questions lean Python-fundamentals first with a focused SQL analytics finish: four Python primitives (heap-backed multi-stream merge for ordered event readers, prefix hash buckets for text autocomplete, hash-table plus two-pointer array intersection without set() , and binary search on the arr[i] - (i + 1) invariant for Nth-missing-integer counting), plus two SQL primitives that test ride-share analytics fluency (first-touch time-series aggregation for new-customers-per-day reports and LAG -driven window functions for consecutive-day buyer cohorts). The framings are mobility data—rider event streams, place autocomplete, route-segment intersection, sparse trip-id arrays, daily new-rider counts, and two-day return retention. This guide walks through the six topic clusters Lyft actually tests, each with a detailed topic explanation , per-sub-topic explanation with a worked example and its solution , and an interview-style problem with a full solution that explains why it works.…