Here's the corrected implementation of the `JobQueue` class with proper concurrency handling using a threading lock to eliminate race conditions:

```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):
        with self.lock:
            if job_id not in self.jobs:
                return False
            data = self.jobs[job_id]

        # Process outside the lock to avoid blocking other threads
        result = processor(data)

        with self.lock:
            self.results[job_id] = result
            del self.jobs[job_id]

        return True

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

### Key Fixes and Design Rationale:

1. **Thread Safety via Locking:**
   - A `threading.Lock()` is used to protect all shared state (`self.jobs`, `self.results`) from concurrent access.

2. **Atomic Job Existence Check:**
   - The existence check and retrieval of job data are performed within a lock to prevent race conditions where a job could be deleted by another thread.

3. **Non-blocking Job Processing:**
   - Actual job processing (`processor(data)`) occurs outside the lock to allow other threads to access and process other jobs concurrently without unnecessary contention.

4. **Safe Result Writing and Cleanup:**
   - The result is stored and the job is removed from the queue within a lock to ensure atomic updates.

5. **Consistent Access Patterns:**
   - All access to shared state (`add_job`, `process_job`, `get_result`) is synchronized, eliminating data races.

### Example Usage:

```python
from threading import Thread
import time

def slow_processor(data):
    time.sleep(0.5)  # Simulate work
    return data * 2

queue = JobQueue()
queue.add_job(1, 10)

def worker():
    queue.process_job(1, slow_processor)

t1 = Thread(target=worker)
t2 = Thread(target=worker)

t1.start()
t2.start()
t1.join()
t2.join()

print(queue.get_result(1))  # Output: 20
```

This implementation ensures thread safety, avoids race conditions, and maintains efficient concurrent execution across multiple threads.