Achieving scalable performance in modern in-memory indexes is primarily limited by synchronization. Traditional synchronization approaches apply a single "one-size-fits-all" strategy, ignoring the diverse characteristics of different index operations. For instance, pessimistic lock coupling forces high atomic overhead on all tree traversals, even simple lookup operations. Meanwhile, optimistic queue-based locking, while efficient for lookups, suffers from performance collapse due to shared data movement during high-contention updates. This paper introduces OPAL, a hybrid operation-aware lock design for modern in-memory indexes. OPAL dynamically selects among three locking mechanisms within a single lock instance based on operation type: (i) optimistic version-based locking for read-only lookups; (ii) lightweight function-pointer-based batching for updates that eliminates shared data movement; and (iii) traditional MCS-based locking for structural modification operations (SMOs), such as node splits and merges, that naturally distributes contention across multiple index nodes. We evaluate OPAL on widely-used index structures: a B+ Tree and an Adaptive Radix Tree (ART). Compared to state-of-the-art optimistic locking, OPAL improves throughput by up to 2.43x and reduces latency by 80%.