I'd love to help you prep for this Senior Data Engineer role at MBDA! It's a fantastic defense engineering company, which means they place a huge premium on data security, high-integrity pipeline systems, and robust data modeling.

To make sure we make the absolute best use of your time, let's start with a quick diagnostic question so I can tailor our practice sessions exactly to your needs.

To help me tailor our prep, how many years of experience do you have as a data engineer, and which area of the interview are you most nervous about?

Transitioning from another tech role is a great advantage—you already have the logical foundation, we just need to adapt it to high-scale data systems. Let's look at what we'll cover to prepare you for MBDA.

Since you are pivoting into data engineering, having a rock-solid grasp of programming fundamentals is where we start. Let's dive into Python proficiency and how writing clean, modular code is evaluated in senior-level interviews.

MBDA places a heavy emphasis on Python, but they evaluate it differently than a standard software engineering firm. Rather than asking you to solve complex algorithmic puzzles or balance binary trees, their interviewers want to see if you can write clean, production-grade code that is resilient, highly testable, and optimized for data transformation.

The Python Pillars in Senior Data Interviews

For a senior role, you will be expected to demonstrate how Python acts as the glue of modern ETL pipelines. This means you should be highly comfortable with three main areas:

1. API Integration and Serialization: Extracting data from REST APIs, handling rate limits, and parsing nested JSON payloads into flat, structured formats.
2. In-Memory Manipulation: Knowing when to use lightweight, fast libraries like Pandas or Polars for local transformations before loading data into a larger analytical system.
3. Production-Ready Scripting: Writing code that does not break silently. This involves implementing rigorous logging, custom exception handling, and highly modular functions that can be easily unit-tested.

Let's look at how we can put these best practices into action. Suppose an interviewer asks you to write a Python script that fetches weather telemetry from an API, parses it, and safely returns a cleaned list of records. A junior developer might write a single, long script with nested loops. As a senior candidate, you want to show a highly modular, decoupled design.

import logging
import requests
from typing import List, Dict, Any

# Configure structured logging for production observability
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)

def fetch_telemetry_data(api_url: str) -> List[Dict[str, Any]]:
    """Fetches raw payload from the target telemetry endpoint."""
    try:
        logger.info(f"Initiating request to {api_url}")
        response = requests.get(api_url, timeout=10)
        response.raise_for_status() # Raises HTTPError for bad responses
        return response.json().get("records", [])
    except requests.exceptions.RequestException as e:
        logger.error(f"API request failed: {e}")
        raise RuntimeError("Failed to retrieve data from source") from e

def clean_record(raw_record: Dict[str, Any]) -> Dict[str, Any]:
    """Parses and cleans a single telemetry record."""
    # Handle missing or malformed keys defensively
    return {
        "device_id": raw_record.get("device_id", "unknown"),
        "reading_value": float(raw_record.get("value", 0.0)),
        "timestamp": raw_record.get("timestamp")
    }

def process_telemetry(api_url: str) -> List[Dict[str, Any]]:
    """Orchestrates the extraction and transformation process."""
    raw_data = fetch_telemetry_data(api_url)
    cleaned_data = [clean_record(record) for record in raw_data if record]
    logger.info(f"Successfully processed {len(cleaned_data)} records.")
    return cleaned_data

Notice how we separated the concerns: fetching data is isolated from cleaning data. We explicitly handle potential network failures with try-except blocks, use type hinting to make our code self-documenting, and include logging so we can track execution in a production orchestrator like Airflow. This is the level of engineering quality interviewers look for.