Summary
I recently interviewed for a Senior Data Engineer position at SmartNews, a prominent news aggregation platform. The interview process spanned several rounds, covering data structures, algorithms, and extensive system design, ultimately resulting in an offer.
Full Experience
My interview journey with SmartNews for the Senior Data Engineer role was comprehensive and challenging, exactly what I expected from a leading tech company. It started with an initial recruiter call, which was a standard discussion about my experience, career aspirations, and what I was looking for in a new role. Following that, I had a technical phone screen. This round focused on data structures and algorithms, where I was given two coding problems to solve within an hour, discussing my thought process and optimizing my solutions.
After successfully clearing the phone screen, I proceeded to the virtual onsite rounds. These consisted of four distinct interviews: two deep-dive System Design rounds, one focusing on Data Structures & Algorithms with a strong emphasis on distributed systems thinking, and a Behavioral round with a hiring manager. The System Design rounds were particularly rigorous, requiring me to design large-scale data pipelines and real-time analytics systems from the ground up, considering scalability, fault tolerance, and various trade-offs. The coding round during the onsite was more complex, involving not just an algorithm but also how it would fit into a larger data processing context. The behavioral interview was a standard discussion about past projects, team collaboration, and problem-solving approaches. Overall, the interviewers were highly skilled, and the discussions were engaging and insightful. I felt well-prepared, and I was thrilled to receive an offer for the role.
Interview Questions (3)
I was presented with a problem to design a data structure that efficiently supports two operations:
add(num): Adds a new number to the data stream.findKthLargest(): Returns the k-th largest element among all elements seen so far.
The interviewer was keen on understanding the time and space complexity of different approaches and how I would optimize it for high-throughput data streams and large values of 'k'.
The challenge was to design a system capable of identifying the top 10 most active users within the last hour, with the list being updated every 5 minutes. The system needed to handle high volumes of log data (timestamp, user_id, event_type) generated from a distributed environment. Key considerations included scalability, fault tolerance, data accuracy, and the latency of updates.
For this System Design round, I was tasked with designing a comprehensive real-time analytics pipeline for user engagement data (clicks, views, purchases) for a large-scale news aggregation platform. The design needed to cover the entire data lifecycle: data sources, ingestion mechanisms, real-time processing logic (e.g., aggregations, feature engineering), storage solutions for both raw and processed data, and how various downstream applications like dashboards and recommendation engines would consume this data. Performance, scalability, and cost-efficiency were critical.
Preparation Tips
My preparation involved a multi-faceted approach. For coding, I focused on LeetCode problems, particularly Medium and Hard questions related to heaps, hash maps, sorting, and graph algorithms, as these are common in data engineering roles. I also reviewed common design patterns for distributed systems and brushed up on my SQL skills, including window functions and query optimization.
For System Design, I extensively studied common design patterns for large-scale data pipelines, including real-time processing, batch processing, data warehousing, and messaging queues. Resources like 'Designing Data-Intensive Applications' and various online articles on real-world system designs were invaluable. I practiced articulating my designs clearly, breaking down complex problems, and discussing trade-offs. For behavioral questions, I used the STAR method to prepare concise stories from my past experiences that highlighted my problem-solving, teamwork, and leadership skills.
Summary
I recently interviewed for a Senior Data Engineer position at SmartNews, which involved multiple rounds focusing on coding, SQL, system design, and behavioral skills. I successfully received an offer for the role.
Full Experience
I recently went through the interview process for a Senior Data Engineer role at SmartNews, and I wanted to share my experience. The process involved several rounds, focusing on my data engineering skills, system design capabilities, and behavioral aspects.
Application and Initial Screen:
I applied directly through their careers portal. A recruiter reached out within a week to schedule an initial phone screen. This call was primarily about my background, interest in SmartNews, and aligning expectations. It lasted about 30 minutes.
Round 1: Technical Screen - Coding & SQL (45 mins)
This round was with a senior engineer. We started with a quick introduction, and then moved into the technical problems.
- Coding Problem: The interviewer asked me to write code to find the
kth largest element in an unsorted array. I discussed various approaches like sorting, min-heap, max-heap, and quickselect. I implemented the quickselect algorithm in Python and walked through its time and space complexity. - SQL Problem: The next question was about SQL. I was given a table
Productswith columnsproduct_id,product_name,category, andprice. The task was to find the top 3 most expensive products in each category. I used window functions (ROW_NUMBER()orRANK()) to solve this.
Round 2: System Design (60 mins)
This round was with an engineering manager. The core problem was to design a real-time analytics dashboard for an e-commerce platform.
- Problem Statement: Design a system that can collect, process, and display real-time sales and user activity data (e.g., page views, clicks, purchases) for an e-commerce website. The dashboard should show metrics like total sales per minute, top-selling products, active users, etc., with low latency.
- Discussion: I started with functional and non-functional requirements, considering data sources (webhooks, Kafka), data ingestion (Kafka, Flink/Spark Streaming), data storage (NoSQL for raw data, OLAP DB for aggregated data like ClickHouse/Druid), and dashboarding tools (Grafana). We discussed scalability, fault tolerance, and consistency trade-offs.
Round 3: Behavioral / Leadership Principles (45 mins)
This round was with a director. It focused heavily on behavioral questions and how my past experiences aligned with their company values.
- Questions:
- "Tell me about a time you had to deal with ambiguity in a project. How did you handle it?"
- "Describe a project where you failed. What did you learn?"
- "How do you prioritize your work when you have multiple competing deadlines?"
Interview Questions (5)
Given an unsorted array of integers nums and an integer k, return the kth largest element in the array. Note that it is the kth largest element in the sorted order, not the kth distinct element.
Given a Products table with columns product_id, product_name, category, and price, write a SQL query to find the top 3 most expensive products in each category. If there are ties in price, all products with the same price at the 3rd rank should be included.
Design a system that can collect, process, and display real-time sales and user activity data (e.g., page views, clicks, purchases) for an e-commerce website. The dashboard should show metrics like total sales per minute, top-selling products, active users, etc., with low latency, high scalability, and fault tolerance.
Tell me about a time you had to deal with ambiguity in a project. How did you handle it?
Describe a project where you failed. What did you learn?
Preparation Tips
My preparation primarily involved grinding LeetCode for coding questions, focusing on medium-to-hard level problems. For SQL, I practiced on HackerRank and LeetCode SQL problems. For system design, I read "Designing Data-Intensive Applications" and watched many YouTube videos on common system design patterns (e.g., GDD, CodeKarle). I also did several mock interviews.