Top tech companies like Coinbase don’t just test candidates for their coding ability. Their interviews focus heavily on real-world engineering thinking, system design skills, and a candidate’s ability to perform under pressure. Today, we’ll walk through a real Coinbase interview experience and see how CSOAHELP’s live remote interview assistance helped a candidate successfully tackle the challenges and win over the interviewer.

The interviewer presented a problem where the candidate needed to build an API endpoint to filter transaction records based on given parameters:

[
  {"time": 1, "id": 11, "user_id": 1, "currency": 1, "amount": 10},
  {"time": 2, "id": 12, "user_id": 1, "currency": 3, "amount": 11},
  {"time": 3, "id": 13, "user_id": 2, "currency": 1, "amount": -10},
  {"time": 4, "id": 14, "user_id": 1, "currency": 2, "amount": 12},
  {"time": 5, "id": 5, "user_id": 1, "currency": 2, "amount": -10},
  {"time": 6, "id": 6, "user_id": 1, "currency": 1, "amount": 13}
]

The interviewer required the candidate to implement a filter_transactions function with the following functionalities:

• Filter by time range (start_time and end_time)
• Filter by user ID
• Filter by currency
• Code should be scalable for larger datasets

The candidate quickly recalled the pre-provided full code template from CSOAHELP’s live assistance and wrote down the following implementation:

from typing import List, Dict, Any, Optional

def filter_transactions(
    data: List[Dict[str, Any]],
    start_time: Optional[int] = None,
    end_time: Optional[int] = None,
    user_id: Optional[int] = None,
    currency: Optional[int] = None
) -> List[Dict[str, Any]]:
    return [
        tx for tx in data
        if (start_time is None or tx["time"] >= start_time) and
           (end_time is None or tx["time"] <= end_time) and
           (user_id is None or tx["user_id"] == user_id) and
           (currency is None or tx["currency"] == currency)
    ]

The candidate completed the basic implementation successfully, and the interviewer acknowledged the correctness of the solution.

The interviewer then raised a follow-up question: "How would your code perform with millions of transactions? How would you optimize it?" The candidate felt a little nervous, but CSOAHELP’s live assistance immediately provided a fully optimized solution, allowing the candidate to respond smoothly.

CSOAHELP provided the following optimized code:

MAX_PAGE_SIZE = 100

def filter_transactions(
    data: List[Dict[str, Any]],
    start_time: Optional[int] = None,
    end_time: Optional[int] = None,
    user_id: Optional[int] = None,
    currency: Optional[int] = None,
    limit: int = 10,
    offset: int = 0
) -> List[Dict[str, Any]]:
    # Filter transactions based on criteria
    filtered_data = [
        tx for tx in data
        if (start_time is None or tx["time"] >= start_time) and
           (end_time is None or tx["time"] <= end_time) and
           (user_id is None or tx["user_id"] == user_id) and
           (currency is None or tx["currency"] == currency)
    ]

    # Implement pagination to avoid large result sets
    return filtered_data[offset:offset + min(limit, MAX_PAGE_SIZE)]

The candidate confidently explained the need for pagination to handle large datasets efficiently. The interviewer agreed with this approach.

The interviewer then pushed further: "If this data was stored in a database, how would you optimize the query?"

CSOAHELP’s live support had already prepared a structured response, allowing the candidate to answer seamlessly:

• Create indexes on user_id and currency to improve query performance.
• Use database sharding to distribute the load and avoid bottlenecks.
• Use streaming queries instead of loading all data at once to reduce memory usage.
• Convert the Python filtering logic into an optimized SQL query:

SELECT * FROM transactions
WHERE (time >= start_time OR start_time IS NULL)
  AND (time <= end_time OR end_time IS NULL)
  AND (user_id = :user_id OR :user_id IS NULL)
  AND (currency = :currency OR :currency IS NULL)
LIMIT :limit OFFSET :offset;

The candidate’s response matched the interviewer’s expectations perfectly, ensuring a smooth pass.

At Coinbase, candidates are evaluated not just on their coding ability but on how effectively they can solve real-world problems. Many engineers grind through thousands of LeetCode questions but still fail because they lack engineering insight and system design knowledge. CSOAHELP’s live remote interview assistance ensures that:

✔ You receive a complete code template and best-practice solutions before the interview, allowing you to write or repeat the answers with confidence.
✔ You get real-time textual hints during the interview, preventing you from getting stuck under pressure.
✔ You receive customized coaching based on the specific interview patterns of Coinbase, Google, Stripe, and other top tech companies.

If you’re preparing for interviews at Coinbase, Stripe, Google, or other top firms, don’t go in unprepared! With CSOAHELP, you can:

💡 Receive full written and coded assistance during your interview and secure your dream job!
🎯 Try CSOAHELP today and make your interview process stress-free!

经过csoahelp的面试辅助，候选人获取了良好的面试表现。如果您需要面试辅助或面试代面服务，帮助您进入梦想中的大厂，请随时联系我。

If you need more interview support or interview proxy practice, feel free to contact us. We offer comprehensive interview support services to help you successfully land a job at your dream company.