Owing to a need for low latency data accesses, emerging IoT and mobile applications commonly require distributed data stores (e.g., key-value or KV stores) to operate entirely at the network's edge. Unfortunately, existing KV stores employ randomized data placement policies (e.g., consistent hashing) that ignore the client mobility and resulting variance in client-server latencies that are inherent to edge applications - -the effect is largely suboptimal and inefficient data placement. We present Portkey, a distributed KV store that dynamically adapts data placement according to time-varying client mobility and data access patterns. The key insight with Portkey is to lean into the inherent mobility and prioritize rapid but approximate placement decisions over delayed optimal ones. Doing so enables the efficient tracking of client-server latencies despite edge resource constraints, and the use of greedy placement heuristics that are self-correcting over short timescales. Results with a realistic autonomous vehicle dataset and two small-scale deployments reveal that Portkey reduces average and tail request latency by 21-82% and 26-77% compared to existing placement strategies.