Top 22 BP Data Engineer Interview Questions + Guide in 2024
Introduction
With over 87,000 employees and around 12 million customer touchpoints, BP is a key player in the global energy market. Spanning 61 countries worldwide, their multinational presence is driven by a vision to progress by incorporating low-carbon energy sources alongside their existing fossil fuel business.
If you have an upcoming data engineer interview at BP, you’ve come to the right spot. As a BP data engineer, you’ll tackle significant responsibilities such as developing data pipelines, maintaining data platforms, and supporting system performance automation and metrics.
With our industry experience and model answers to common BP data engineer interview questions, you’ll approach the process at BP with increased confidence.
BP Data Engineer Interview Process
Regardless of the role and position you’re applying for at BP, the interviewers will take a structural approach to evaluating your applied, communicational, and technical skills. Be prepared to go through multiple rounds of rigorous discussion to land the role of data engineer at BP.
Submitting the Application
Depending on your position in the job market, you can either apply through the career portal or wait for a BP recruiter to contact you. A talent acquisition team and the hiring manager will review your CV, contact details, and answers to the technical questions on the application.
Initial Telephone Interview
If your CV is shortlisted, you’ll receive a confirmation email or call. A contact from the acquisition team will likely schedule a telephone interview to ask pre-determined questions about your previous job experience and soft skills. Also, expect a few behavioral questions. Your answers will be screened and verified before promotion to the next round.
Technical Video/Telephone Interview
In this round, you’ll be asked questions about data engineering, including programming languages, data structures, and data pipelines. You also may be given take-home challenges and case studies.
On-Site Interview
Assuming success in the last round, you’ll be invited for an on-site interview (or a virtual equivalent) with a panel of data engineers, data science managers, or other relevant personnel. Expect in-depth technical discussions, behavioral questions, and more case studies.
Psychometric Tests
A series of psychometric tests may also be given to determine your cognitive abilities. However, these are only implemented for roles that require frequent interaction with external stakeholders and other teams.
If you nail the interview, expect a call from your recruiter with an offer. Once you accept, they’ll drop you a confirmation email and start the onboarding process.
What Questions Are Asked in a BP Data Engineer Interview?
Slightly deviating from the industry norms of prioritizing generic programming questions, BP interviewers generally design their questions around algorithms and SQL. This section discusses a few recurring behavioral and technical questions in data engineer interviews at BP.
1. How do you stay organized when you have multiple deadlines?
This question will evaluate your ability to manage time effectively and stay organized under pressure. This is crucial for a data engineer to meet project deadlines in BP’s fast-paced environment.
How to Answer
Explain a system you use to prioritize tasks, such as creating a timeline, breaking down tasks into smaller ones, and using tools like to-do lists or project management software.
Example
“I stay organized by breaking down tasks into smaller, manageable steps and setting deadlines for each. I prioritize tasks based on their importance and urgency, using a combination of project management software and daily to-do lists. This helps me stay focused and ensures I meet deadlines, even when juggling multiple projects.”
2. What are you looking for in your next job?
This question aims to draw out your motivations for joining BP to assess your alignment with the company’s values, goals, and culture.
How to Answer
Highlight what attracts you to BP, such as its commitment to innovation, sustainability efforts, career growth opportunities, and alignment with your values. Discuss how you see yourself contributing to and benefiting from the company’s mission.
Example
“I’m drawn to BP because of its strong focus on innovation and sustainability in the energy sector. I’m excited about the opportunity to work on cutting-edge projects with a positive environmental impact. Additionally, I admire BP’s commitment to diversity and inclusion, and I’m eager to contribute my skills and experiences to a company that values these principles.”
3. Tell me about a time when you exceeded expectations during a project. What did you do, and how did you accomplish it?
The interviewer at BP will assess your ability to go above and beyond in your work, demonstrating initiative, problem-solving skills, and commitment to achieving results.
How to Answer
Describe a specific project or task where you not only met expectations but exceeded them. Explain the challenges, your actions to overcome them, and the positive outcomes.
Example
“In a previous project, I exceeded expectations by implementing a more efficient data processing system. Despite tight deadlines and technical challenges, I conducted thorough research, collaborated with team members, and proposed innovative solutions. As a result, we not only met project goals ahead of schedule but also improved data accuracy by 25%, leading to significant cost savings for the company.”
4. Tell me about a time you had to learn a new skill or process quickly. How did you approach the learning process, and what was the result?
This question will evaluate your adaptability and ability to acquire new skills or knowledge rapidly, which are essential in a dynamic field like data engineering at BP.
How to Answer
Discuss a situation in which you had to learn a new skill or process quickly. Discuss your steps to learning it, such as seeking resources, hands-on practice, and asking for feedback. Highlight the positive outcomes or results of your learning efforts.
Example
“When I encountered a new data analysis tool in a project, I immediately immersed myself in online tutorials, documentation, and practical exercises to understand its functionalities. I also asked for guidance from colleagues and applied the tool to our project. As a result, I became proficient in the tool within a week, enabling me to streamline our data analysis process and deliver insights to stakeholders ahead of schedule.”
5. Imagine you’re leading a brainstorming session, but everyone seems stuck and uninspired. How would you spark creativity and get the ideas flowing again?
This question assesses your ability to facilitate creative thinking and problem-solving within a team, essential for generating innovative solutions.
How to Answer
Emphasize the importance of diversity of thought and brainstorming without judgment. Describe techniques you would use to stimulate creativity, such as encouraging open dialogue, incorporating visual aids or analogies, and fostering a collaborative and supportive environment.
Example
“To spark creativity in a brainstorming session, I would encourage everyone to share their ideas freely without fear of criticism. I might introduce an icebreaker activity to loosen up the atmosphere and get people thinking outside the box. Additionally, I would use visual aids like mind maps or mood boards to stimulate new ideas and encourage collaboration. By creating a supportive and inclusive environment, we will be more effective at coming up with innovative solutions that address our challenges.”
6. You have an array of integers, nums of length n spanning 0 to n with one missing. Write a function missing_number that returns the missing number in the array.
Note: Complexity of O(n) required.
Example:
Input:
nums = [0,1,2,4,5]
missing_number(nums) -> 3
The interviewer at BP may ask this question to check your problem-solving skills, understanding of array manipulation, and ability to optimize code.
How to Answer
Approach this problem using arithmetic progression and exploiting the sum formula for consecutive integers. Calculate the expected sum of numbers from 0 to n, then subtract the sum of elements in the array to find the missing number.
Example
def missing_number(nums):
n = len(nums)
total = n*(n+1)/2
sum_of_nums = sum(nums)
return total - sum_of_nums
7. You are given a dictionary with two keys a and b that hold integers as their values. Without declaring any other variable, swap the value of a with the value of b and vice versa.
Note: Return the dictionary after editing it.
Example:
Input:
numbers = {
'a':3,
'b':4
}
Output:
def swap_values(numbers) -> {'a':4,'b':3}
Your understanding of dictionary manipulation and proficiency in Python’s variable assignment will be assessed through this problem.
How to Answer
Use Python’s addition and subtraction functions to swap the values of keys ‘a’ and ‘b’ in the dictionary directly. You may also use the tuple unpacking function to do the same.
Example
Addition and subtraction function:
def swap_values(numbers):
numbers['a'] = numbers['a'] + numbers['b']
numbers['b'] = numbers['a'] - numbers['b']
numbers['a'] = numbers['a'] - numbers['b']
return numbers
Tuple unpacking function:
def swap_values(numbers):
numbers['a'], numbers['b'] = numbers['b'], numbers['a']
return numbers
8. Find and return all the prime numbers in an array of integers. If there are no prime numbers, return an empty array.
Example:
Input:
[1, 2, 3]
Output:
[2,3]
You may be asked this to demonstrate your understanding of basic number theory and algorithmic efficiency as a data engineer.
How to Answer Check if a number is prime by finding out if it’s divisible by lower numbers. Iterate through the array to check each number.
Example
import math
def get_prime_numbers(nums):
primes = []
for num in nums:
if num < 2:
continue
is_prime = True
for i in range(2, int(math.sqrt(num)) + 1):
if num % i == 0:
is_prime = False
break
if is_prime:
primes.append(num)
return primes
9. You are given an N-dimensional array (a nested list), and your task is to convert it into a 1D array. The N-dimensional array can have any number of nested lists, and each nested list can contain any number of elements. The elements in the nested lists are integers. Write a function that takes an N-dimensional array as input and returns a 1D array.
Example 1:
Input:
array = [1, [2, 3], [4, [5, 6]], 7]
Output:
flatten_array(array) -> [1, 2, 3, 4, 5, 6, 7]
Example 2:
Input:
array = [[1, 2], [3, 4], [5, 6]]
Output:
flatten_array(array) -> [1, 2, 3, 4, 5, 6]
This question evaluates your understanding of recursion or iteration and array manipulation. An interviewer at BP may ask it to assess your ability to handle nested data structures and write efficient algorithms.
How to Answer
Implement a recursive function to flatten the N-dimensional array into a 1D array. Iterate through each element of the input array, recursively flattening nested lists and appending integers to the result.
Example
def flatten_array(array):
result = []
for i in array:
if isinstance(i, list):
result.extend(flatten_array(i))
else:
result.append(i)
return result
10. You are testing hundreds of hypotheses with many t-tests. What considerations should be made?
This question will assess your understanding of statistical hypothesis testing and the challenges associated with multiple hypothesis testing.
How to Answer
Discuss considerations such as adjusting significance levels (e.g., Bonferroni correction), controlling false discovery rate, using more stringent criteria for hypothesis testing, and interpreting results in the context of multiple comparisons.
Example
“In testing numerous hypotheses with t-tests, I would first adjust significance levels to counteract the increased chance of false positives. Employing techniques such as the Bonferroni correction can help maintain an appropriate overall Type I error rate. Furthermore, I’d focus on controlling the false discovery rate to mitigate the risk of making incorrect rejections. Using more stringent criteria during hypothesis testing is also crucial, as it increases the reliability of the findings and minimizes the occurrence of false positives.”
11. We’re given a table of bank transactions with three columns, user_id, a deposit or withdrawal value (determined if the value is positive or negative), and created_at time for each transaction. Write a query to get the total three-day rolling average for deposits by day.
Note: Please use the format '%Y-%m-%d' for the date in the output.
Example:
Input:
bank_transactions table
| Column | Type |
|---|---|
| user_id | INTEGER |
| created_at | DATETIME |
| transaction_value | FLOAT |
Output:
| Column | Type |
|---|---|
| dt | VARCHAR |
| rolling_three_day | FLOAT |
Your interviewer at BP may ask this question to gauge your ability as a data engineer to work with time-series data and perform calculations within SQL databases.
How to Answer
Approach this problem by using window functions in SQL to calculate the rolling three-day average for deposits. Use self-join to have three rows of the last three days for each datetime. Then sum the values.
Example
WITH valid_transactions AS (
SELECT DATE_FORMAT(created_at, '%Y-%m-%d') AS dt
, SUM(transaction_value) AS total_deposits
FROM bank_transactions AS bt
WHERE transaction_value > 0
GROUP BY 1
)
SELECT vt2.dt,
AVG(vt1.total_deposits) AS rolling_three_day
FROM valid_transactions AS vt1
INNER JOIN valid_transactions AS vt2
-- set conditions for greater than three days
ON vt1.dt > DATE_ADD(vt2.dt, INTERVAL -3 DAY)
-- set conditions for max date threshold
AND vt1.dt <= vt2.dt
GROUP BY 1
12. Let’s say we have a table with id and name fields. The table holds over 100 million rows, and we want to sample a random row in the table without throttling the database. Write a query to randomly sample a row from this table.
Input:
big_table table
| Columns | Type |
|---|---|
| id | INTEGER |
| name | VARCHAR |
This question checks your understanding of SQL query optimization and random row selection techniques.
How to Answer
Approach this problem by using a combination of SQL functions to generate a random sample and select a single row based on that random sample.
Example
SELECT r1.id, r1.name
FROM big_table AS r1
INNER JOIN (
SELECT CEIL(RAND() * (
SELECT MAX(id)
FROM big_table)
) AS id
) AS r2
ON r1.id >= r2.id
ORDER BY r1.id ASC
LIMIT 1
13. Describe an algorithm to efficiently transform raw data from a transactional database into a denormalized format suitable for analytical processing.
Your interviewer at BP may ask this question to evaluate your understanding of data transformation techniques as a data engineer.
How to Answer
Describe an algorithm that efficiently aggregates and joins data from multiple transactional tables, denormalizing them into a single table optimized for analytical queries.
Example
“One possible approach involves using batch processing techniques with optimized data structures like columnar storage formats. By aggregating and joining related data during the transformation process and using parallel processing, we can efficiently denormalize the data.”
14. Explain the breadth-first search (BFS) and depth-first search (DFS) algorithms.
The BP interviewer may ask this question to evaluate your knowledge of algorithms commonly used in data processing and analysis tasks as a data engineer.
How to Answer
Explain the basic principles of BFS and DFS algorithms, including their traversal order and applications in various problem domains.
Example
“BFS explores nodes level by level, while DFS checks as far as possible along each branch before backtracking. BFS is similar to systematically exploring a maze, where I would first check all paths at the current level before moving to the next. In programming terms, I’d use a queue to keep track of the nodes to be visited next. DFS, however, is more like plunging into one path until I can’t go any further, then retracing my steps. BFS suits tasks like finding the shortest path in a network, while DFS is handy for problems like maze-solving or identifying connected components in a graph.”
15. Given a large dataset containing sales records with timestamps, design an algorithm to identify periods of peak sales activity.
Your ability to design algorithms for analyzing time-series data and identifying patterns will be assessed by the interviewer at BP.
How to Answer
Propose an algorithm that identifies periods of peak sales activity by looking at sales records’ timestamps and locating significant increases in sales volume within specific time intervals.
Example
“To identify peak sales periods, I’d implement a sliding window algorithm over the sales records’ timestamps. Let’s say I choose a weekly window. Within each week, I’d calculate metrics like the mean and standard deviation of sales volume. Any week with sales significantly above the mean plus a certain number of standard deviations could be considered a peak sales period. This method helps capture seasonal variations and sudden spikes in sales activity, providing insights for resource allocation and marketing strategies.”
16. What are window functions in SQL, and how are they used in data analysis tasks? Provide examples of scenarios where window functions can be applied to derive meaningful insights from datasets.
As a data engineer, your understanding of window functions in SQL and their application in data analysis tasks will be evaluated with this question by the BP interviewer.
How to Answer
Explain window functions in SQL and how they allow you to perform calculations across a set of rows related to the current row without the need for self-joins or subqueries.
Example
“Window functions like ROW_NUMBER(), RANK(), and LAG() perform calculations within defined windows of data. For instance, to identify top-performing sales regions by comparing each region’s sales to the overall average, we could use a window function to calculate the average sales across all regions and then compare each region’s sales to this average.”
17. Discuss an approach to developing an algorithm to predict future energy consumption trends based on historical data from BP Power’s facilities.
Your interviewer may ask this question to evaluate your understanding of time-series forecasting techniques and their application in predicting energy consumption trends.
How to Answer
To develop an algorithm for predicting future energy consumption trends, you would typically preprocess historical energy consumption data, choose an appropriate predictive model, train it, validate its performance, and then use it to forecast future energy consumption trends.
Example
“One approach would involve preprocessing historical energy consumption data, such as aggregating it into appropriate time intervals (e.g., hourly, daily), handling missing values, and identifying trends and seasonality. Then, we could use a machine learning model like an autoregressive integrated moving average (ARIMA) or a long short-term memory (LSTM) neural network to forecast future energy consumption based on past patterns and external factors such as weather data.”
18. Describe an algorithm to optimize energy distribution across BP Power’s grid network to minimize transmission losses and ensure reliable delivery to customers.
This question evaluates your ability to design algorithms for optimizing energy distribution in power grid networks.
How to Answer
An effective algorithm for optimizing energy distribution in grid networks would involve analyzing network topology, load distribution, and power flow constraints to determine optimal routing and voltage levels.
Example
“My approach would involve using mathematical optimization techniques like linear programming or convex optimization to minimize transmission losses while meeting demand and maintaining system stability. This algorithm would consider factors such as load demand, network topology, generation capacity, and transmission constraints to determine the optimal allocation of energy across the grid network.”
19. Discuss techniques for optimizing database performance in SQL Server, focusing on query optimization, indexing strategies, and database schema design.
This question may be asked in your BP data engineer interview to check your knowledge of database performance optimization techniques, focusing on query optimization, indexing strategies, and database schema design in SQL Server.
How to Answer
Techniques for optimizing database performance in SQL Server include query optimization through proper indexing, query tuning, and avoiding unnecessary operations.
Example
“One effective technique is to create indexes on columns frequently used in queries to speed up data retrieval. This could involve using clustered and non-clustered indexes, covering indexes, and filtered indexes based on query patterns and data distribution. Additionally, partitioning large tables can improve query performance by reducing the amount of data processed for each query.”
20. Explain indexing in databases. Discuss different types of indexes and scenarios where they would be beneficial.
Your interviewer may ask this question to assess your knowledge of database optimization techniques and your ability to design efficient database schemas.
How to Answer
Explain indexing, provide examples, and discuss scenarios where different types of indexing could be beneficial.
Example
“Indexing in databases involves creating data structures to improve data retrieval speed and efficiency. Different types of indexes serve various purposes and offer benefits such as faster query execution, efficient data retrieval, and support for specific query operations.
For instance, a B-tree index is well-suited for range queries and equality searches, while a hash index is efficient for exact match searches. Full-text indexes are beneficial for searching text fields for specific words or phrases. By carefully selecting and implementing appropriate indexes based on query patterns and workload characteristics, database performance can be significantly enhanced.”
21. We have a table with an id and name fields, holding over 100 million rows. Write a query to randomly sample a row from this table.
This question is likely asked in a BP Data Engineer interview to assess your understanding of efficient data retrieval from large datasets. Sampling a random row from a table with over 100 million rows without causing performance issues requires knowledge of SQL optimization and database indexing.
How to Answer
To answer this question, emphasize the need for efficiency with large datasets. Start by noting that while ORDER BY RAND() is easy to implement, it’s not scalable. Then, briefly explain how using RAND() to generate a random id, combined with a join and LIMIT 1, provides a more efficient way to sample a row.
Example
“To address this problem, I would steer away from using ORDER BY RAND() since it’s not practical for large datasets and can significantly slow down the database. Instead, I’d generate a random value that maps to an existing id in the table, allowing for a more efficient retrieval. This method involves joining back to the table on this random id, ensuring that I can quickly select a row even if there are gaps in the id sequence. By focusing on optimizing performance, I can sample a random row from a table with millions of entries without causing any noticeable strain on the database.”
22. Given a string str of any length, write an algorithm max_repeating to return which character has the longest string of continuous repetition.
This question is likely asked in a BP Data Engineer interview to assess the candidate’s problem-solving skills, particularly in dealing with string manipulation and pattern recognition—key aspects of data processing and transformation.
How to Answer
When answering this question, focus on explaining your approach clearly and efficiently. Start by describing how you would track the longest sequence of repeating characters, emphasizing the importance of minimizing complexity by iterating through the string only once. Mention key steps, such as using pointers to manage the current character and its repetition count, and explain how you handle edge cases, like ties.
Example
“To solve this problem, I would focus on efficiently tracking the longest sequence of repeating characters by using two pointers. One pointer would loop through the string, while the other would keep count of the current character’s repetitions. If a longer repetition is found, I will update my result accordingly. In the case of a tie, I would return the character that appears first.”
How to Prepare for the Data Engineer Role at BP
To prepare for the data engineer interview at BP, develop your programming efficiency, understanding of algorithms, and mastery of database design and maintenance. Communication and problem-solving skills are also essential. In this section, we share a few tips to help you crack your upcoming data engineer interview.
Understand the Job Description and BP
Examine BP’s vision and beliefs to determine your approach to the screening questions. Curate your behavioral answers and scenario-based experiences to ace the interview.
Also, explore our data engineer interview questions designed to challenge you further.
Develop Programming Skills
Data engineering is a tech-heavy field, and you must have at least a basic understanding of programming languages, especially Python. Since algorithms are an integral part of being a data engineer, solve a lot of data engineer Python questions to stay ahead of other candidates.
Refine Your Database Knowledge
Database technologies are rapidly evolving to suit the demands of massive data lake servers and machine learning efforts. Familiarize yourself with database technologies like SQL and MySQL, and understand cloud computing platforms like AWS, Azure, and Google Cloud. Also, to familiarize yourself with the concepts, allocate time for time solving SQL interview questions.
Moreover, stay active in learning new technologies in data engineering, such as data visualization and ETL processes, to help you excel in your job interview for BP.
Participate in Mock Interviews
Mock interviews are among the best methods for refining communication skills and addressing any loopholes in understanding. We conduct P2P mock interviews to help our candidates prepare for particular roles and positions.
FAQs
How much do data engineers at BP make?
We don’t have enough data from BP employees to answer the question. If you have any information regarding an average BP data engineer’s salary, don’t hesitate to leave information with us. To gain further insight about industry averages, head over to our data engineer salary guide.
Where can I read about other candidates’ experiences interviewing for the BP data engineer role?
You can read about other candidates’ experiences on our Slack channel, where the members candidly discuss what they liked and disliked. Feel free to also contribute to the community through this channel!
Does Interview Query have job postings for the BP data engineer role?
Yes, our Jobs Board lists the latest data engineer roles at BP. You can check the eligibility criteria, contact the hiring person, and directly apply for the job. Keep in mind that specific jobs are subject to availability.
The Bottom Line
The interview process for the data engineer position at BP typically involves questions spanning data engineering projects, database management, programming, case studies, and system architecture. Additionally, expect a few behavioral questions related to your professional background. To excel in the interview, enhance your communication and analytical abilities, engage in mock interviews, and tackle various interview practice problems.
Check out our main BP interview guide for more info, and consider applying for other roles, including the data analyst and data scientist positions.
Let us know how your interview went and how our guides helped increase your confidence. We’re eager to hear your success story. All the best!