Here's the complete fixed implementation. The race condition is resolved by introducing a `threading.Lock` that protects all accesses to the shared `self.jobs` and `self.results` dictionaries. The lock is acquired only around the critical sections (dictionary reads/writes) to avoid blocking the potentially long-running `processor(data)` function.

```python
import threading

class JobQueue:
    def __init__(self):
        self.jobs = {}
        self.results = {}
        self.lock = threading.Lock()

    def add_job(self, job_id, data):
        with self.lock:
            self.jobs[job_id] = data
        return job_id

    def process_job(self, job_id, processor):
        # Acquire lock to safely check and remove the job
        with self.lock:
            if job_id not in self.jobs:
                return False
            data = self.jobs.pop(job_id)
            
        # Process outside the lock to avoid holding it during long operations
        result = processor(data)
        
        # Acquire lock to safely store the result
        with self.lock:
            self.results[job_id] = result
            
        return True

    def get_result(self, job_id):
        with self.lock:
            return self.results.get(job_id)
```

### Key Fixes:
1. **Added `self.lock = threading.Lock()`** in `__init__` to manage thread synchronization.
2. **Protected dictionary operations** with `with self.lock:` to ensure atomicity. This prevents:
   - A thread from reading a job that another thread is simultaneously deleting.
   - Concurrent writes to `self.results` from causing data corruption or lost updates.
   - `get_result()` reading inconsistent state while another thread is updating the dictionaries.
3. **Used `.pop(job_id)`** instead of separate check/delete to guarantee the "check-and-remove" sequence is atomic under the lock.
4. **Kept the lock duration minimal** by releasing it before calling `processor(data)`, which is crucial for performance since job processing is typically the longest step.