🚀 Cracking the Morgan Stanley Senior Data Engineer Interview

By otakutechie • • 11 min read
A diverse group engaged in a business meeting in a modern office setting.

Why This Matters

Landing a Senior Data Engineer role at Morgan Stanley isn’t just about technical skills—it’s about demonstrating depth, problem-solving ability, and real-world experience. After going through their rigorous interview process, I’m sharing everything I learned to help you prepare effectively.

Interview Timeline: 2 weeks from initial contact to final round
Total Rounds: HR Screen + 2 Technical Rounds
Overall Difficulty: ⭐⭐⭐⭐ (4/5)

📅 Phase 1: The Initial Contact & Scheduling

What Happened

The HR team reached out via Naukri with a clear job description and asked about my availability. Within 48 hours of expressing interest, I had:

✅ Green Flags I Noticed

💡 Preparation Tips

  1. Research the team: Check LinkedIn for your interviewers’ backgrounds
  2. Review the JD thoroughly: Map your experience to their requirements
  3. Prepare your setup: Test your internet, camera, and screen-sharing beforehand

🎯 Round 1: Technical Deep Dive with VP (60 minutes)

Interviewer: Vice President, Data Engineering
Format: Video call with live coding
Difficulty: ⭐⭐⭐ (3/5)

Part 1: Python Fundamentals (20 minutes)

Question 1: Explain Decorators

What they asked: “Can you explain what decorators are in Python and write a simple example?”

My Approach:

# I explained decorators as functions that modify other functions
def timer_decorator(func):
    import time
    def wrapper(*args, **kwargs):
        start = time.time()
        result = func(*args, **kwargs)
        end = time.time()
        print(f"{func.__name__} took {end-start:.2f} seconds")
        return result
    return wrapper

@timer_decorator
def process_data(data):
    # Simulate data processing
    return [x * 2 for x in data]

# Usage
result = process_data([1, 2, 3, 4, 5])

Key Points I Emphasized:

Question 2: Lists vs Tuples

What they asked: “When would you use a list versus a tuple in a data pipeline?”

My Response:

Question 3: List Comprehensions & Generators

They also asked about memory optimization, so I discussed:

# List comprehension - loads all into memory
squares_list = [x**2 for x in range(1000000)]

# Generator - memory efficient for large datasets
squares_gen = (x**2 for x in range(1000000))

Difficulty Rating: Easy-Medium
Time Spent: 10 minutes

Part 2: SQL & Database Design (20 minutes)

Question 1: SQL Joins

Scenario: Given two tables – employees and departments

-- They asked me to demonstrate different join types

-- INNER JOIN: Only matching records
SELECT e.name, d.dept_name
FROM employees e
INNER JOIN departments d ON e.dept_id = d.id;

-- LEFT JOIN: All employees, even without departments
SELECT e.name, d.dept_name
FROM employees e
LEFT JOIN departments d ON e.dept_id = d.id;

-- I explained when to use each in real scenarios

Question 2: Find Second Highest Salary

The Challenge: “Write a query to find the second-highest salary in the employees table.”

My Solution:

-- Method 1: Using LIMIT and OFFSET
SELECT DISTINCT salary
FROM employees
ORDER BY salary DESC
LIMIT 1 OFFSET 1;

-- Method 2: Using subquery (more robust)
SELECT MAX(salary) as second_highest
FROM employees
WHERE salary < (SELECT MAX(salary) FROM employees);

-- Method 3: Using DENSE_RANK (what I recommended)
WITH ranked_salaries AS (
    SELECT 
        salary,
        DENSE_RANK() OVER (ORDER BY salary DESC) as rank
    FROM employees
)
SELECT salary
FROM ranked_salaries
WHERE rank = 2;

Why DENSE_RANK?

Difficulty Rating: Medium
Time Spent: 20 minutes

Part 3: Snowflake Architecture & Optimization (20 minutes)

This was the most challenging part, requiring deep Snowflake knowledge.

Question 1: Snowflake Stages

What they asked: “How do you load data into Snowflake? Explain stages.”

My Explanation:

-- External Stage (S3/Azure/GCP)
CREATE STAGE my_s3_stage
    URL = 's3://mybucket/data/'
    CREDENTIALS = (AWS_KEY_ID='...' AWS_SECRET_KEY='...');

-- Internal Stage
CREATE STAGE my_internal_stage;

-- Loading data using COPY INTO
COPY INTO my_table
FROM @my_s3_stage/file.csv
FILE_FORMAT = (TYPE = CSV FIELD_OPTIONALLY_ENCLOSED_BY = '"');

Real-World Context I Shared:

Question 2: Snowflake Streaming

Topic: Real-time data ingestion

Key Points Discussed:

-- Example: Creating a stream for CDC
CREATE STREAM customer_stream ON TABLE customers;

-- Task to process changes
CREATE TASK process_changes
    WAREHOUSE = compute_wh
    SCHEDULE = '1 MINUTE'
AS
INSERT INTO customer_changes
SELECT * FROM customer_stream WHERE METADATA$ACTION = 'INSERT';

Question 3: Query Optimization (The Critical Part)

Scenario: “A query is running for 30 minutes. How do you optimize it?”

My Troubleshooting Framework:

Step 1: Diagnose

-- Check query profile
SELECT * FROM TABLE(INFORMATION_SCHEMA.QUERY_HISTORY())
WHERE query_id = 'xxx';

-- Analyze execution plan

Step 2: Common Issues & Solutions

IssueSolution
Full table scanAdd clustering keys
Large intermediate resultsUse CTEs, break into steps
Cross joinsRewrite join logic
No pruningPartition by date/key columns

Step 3: Warehouse Scaling

When to SCALE OUT (add clusters):

When to SCALE UP (larger warehouse):

-- Scale up
ALTER WAREHOUSE compute_wh SET WAREHOUSE_SIZE = 'XLARGE';

-- Scale out (multi-cluster)
ALTER WAREHOUSE compute_wh SET 
    MIN_CLUSTER_COUNT = 1
    MAX_CLUSTER_COUNT = 5
    SCALING_POLICY = 'STANDARD';

Real Example I Shared:

Difficulty Rating: Hard
Time Spent: 35 minutes (went over slightly)

⚙️ Round 2: Advanced Problem-Solving (60 minutes)

Interviewer: Senior Data Engineer + Manager
Format: Collaborative problem-solving session
Difficulty: ⭐⭐⭐⭐ (4/5)

Part 1: Project Deep Dive (20 minutes)

What they asked: “Walk me through your most complex data pipeline.”

My Presentation Structure:

  1. Business Problem: Real-time fraud detection for transactions
  2. Architecture:
    • Source: Kafka streams (20K events/sec)
    • Processing: PySpark on Databricks
    • Storage: Snowflake
    • Serving: REST API + Dashboard
  3. Challenges & Solutions:
    • Challenge: Late-arriving data
    • Solution: Watermarking with 5-minute grace period
  4. Results: Reduced false positives by 40%

Follow-up Questions:

💡 Pro Tip: Use the STAR method (Situation, Task, Action, Result) for project discussions.

Part 2: Advanced SQL Challenge (40 minutes)

The Scenario: Given three tables:

-- employees table
CREATE TABLE employees (
    emp_id INT,
    emp_name VARCHAR(100),
    dept_id INT,
    salary DECIMAL(10,2),
    manager_id INT
);

-- departments table
CREATE TABLE departments (
    dept_id INT,
    dept_name VARCHAR(100)
);

-- salaries table (historical)
CREATE TABLE salary_history (
    emp_id INT,
    salary DECIMAL(10,2),
    effective_date DATE
);

Challenge 1: Highest Salary per Department

Question: “Find the employee with the highest salary in each department.”

My Solution:

-- Approach 1: Using ROW_NUMBER (my initial answer)
WITH ranked_employees AS (
    SELECT 
        e.emp_id,
        e.emp_name,
        d.dept_name,
        e.salary,
        ROW_NUMBER() OVER (PARTITION BY e.dept_id ORDER BY e.salary DESC) as rn
    FROM employees e
    JOIN departments d ON e.dept_id = d.dept_id
)
SELECT emp_id, emp_name, dept_name, salary
FROM ranked_employees
WHERE rn = 1;

They Asked: “What if multiple employees have the same highest salary?”

My Improved Solution:

-- Approach 2: Using DENSE_RANK to handle ties
WITH ranked_employees AS (
    SELECT 
        e.emp_id,
        e.emp_name,
        d.dept_name,
        e.salary,
        DENSE_RANK() OVER (PARTITION BY e.dept_id ORDER BY e.salary DESC) as rank
    FROM employees e
    JOIN departments d ON e.dept_id = d.dept_id
)
SELECT emp_id, emp_name, dept_name, salary
FROM ranked_employees
WHERE rank = 1;

-- This returns ALL employees with the highest salary if there are ties

Performance Discussion:

Challenge 2: Salary Growth Analysis

Question: “Calculate year-over-year salary growth for each employee.”

My Solution:

WITH salary_growth AS (
    SELECT 
        emp_id,
        YEAR(effective_date) as year,
        salary,
        LAG(salary) OVER (PARTITION BY emp_id ORDER BY effective_date) as prev_salary,
        LAG(YEAR(effective_date)) OVER (PARTITION BY emp_id ORDER BY effective_date) as prev_year
    FROM salary_history
)
SELECT 
    emp_id,
    year,
    salary as current_salary,
    prev_salary,
    ROUND(((salary - prev_salary) / prev_salary * 100), 2) as growth_percentage,
    year - prev_year as years_gap
FROM salary_growth
WHERE prev_salary IS NOT NULL
ORDER BY emp_id, year;

Key Concepts Demonstrated:

Challenge 3: Manager Hierarchy

Bonus Question: “Find all employees who earn more than their manager.”

My Solution:

SELECT 
    e.emp_name as employee_name,
    e.salary as employee_salary,
    m.emp_name as manager_name,
    m.salary as manager_salary,
    (e.salary - m.salary) as salary_difference
FROM employees e
JOIN employees m ON e.manager_id = m.emp_id
WHERE e.salary > m.salary
ORDER BY salary_difference DESC;

Follow-up: “How would you optimize this for millions of records?”

My Answer:

  1. Index on manager_id
  2. Materialized view if query runs frequently
  3. Consider incremental updates instead of full scans

📊 Difficulty Breakdown

TopicDifficultyTime %Importance
Python Basics⭐⭐15%High
SQL Fundamentals⭐⭐⭐25%Critical
Advanced SQL⭐⭐⭐⭐30%Critical
Snowflake⭐⭐⭐⭐20%High
Projects⭐⭐⭐10%Medium

🎯 Your Preparation Checklist

Week 1-2: Fundamentals

Week 3: Advanced Topics

Week 4: Mock Interviews

⚠️ Common Pitfalls to Avoid

Technical Mistakes:

  1. Not asking clarifying questions – Always confirm assumptions about data
  2. Jumping to code immediately – Discuss approach first
  3. Ignoring edge cases – What if data is NULL? Empty? Duplicates?
  4. Not explaining trade-offs – Every solution has pros/cons

Communication Mistakes:

  1. Going silent while coding – Think out loud, explain your reasoning
  2. Not admitting when stuck – It’s okay to ask for hints
  3. Over-explaining basic concepts – Be concise, go deep only when asked
  4. Forgetting to relate to real experience – Connect theory to your work

🏆 Red Flags vs Green Flags

🚩 Red Flags I Was Alert For:

✅ Green Flags I Noticed:

💭 My Honest Reflections

What Went Well:

What I’d Improve:

Was It Worth It?

Absolutely. Even if the outcome varies, the preparation alone made me a better engineer. The interview pushed me to really understand concepts I’d been using superficially.

🎓 Final Thoughts & Advice

For Junior Engineers: This role requires 4-6 years of solid experience. Focus on mastering one cloud data warehouse (Snowflake/Redshift/BigQuery) deeply before interviewing.

For Mid-Level Engineers: Your project experience matters most. Have 3-4 detailed stories ready about complex problems you’ve solved.

For Senior Engineers: They expect you to justify architectural decisions and discuss trade-offs. Brush up on cost optimization and performance tuning.

Universal Tips:

  1. Practice live coding – It’s different from coding alone
  2. Learn the fundamentals deeply – Don’t just memorize solutions
  3. Connect everything to real work – Theory + Practice = Success
  4. Be honest about gaps – “I haven’t used X, but here’s how I’d approach it”

📬 What’s Next?

After Round 2, Still waiting for feedback.

Questions for you:

Connect with me if you have questions or want to discuss your preparation strategy!

Good luck with your interviews! Remember: Preparation beats anxiety every single time. 🚀

by Gayle Laakmann McDowell :: Cracking The Coding Interview: 189 ...

by Gayle Laakmann McDowell :: Cracking The Coding Interview: 189 …

₹432
Buy on Amazon

Advertisement

300×250 Medium Rectangle

Related

Leave a Reply

Your email address will not be published. Required fields are marked *