Chariot

Role: Data Engineer (Consultant)

College: Not Specified

Interview Date: November 2023 (Coding test development date mentioned)

Interview Type: Full-Time

Result: Not Specified

Difficulty: Medium to Hard

Rounds: 5 rounds

Topics Asked: Data engineering, AWS services (Kinesis, S3, Lambda, Redshift), programmatic transforms, schema validation, data quality, debugging, SQL queries, infrastructure as code (Terraform, CloudFormation), SRE principles, Python, communication, problem-solving, solution design.

Experience:

This experience is based on Chariot Solutions' data engineering interview process, which includes a specific coding challenge designed to be relevant to day-to-day work, followed by further technical evaluation.

Round 1: Pre-Screen (30 minutes)
A casual introductory chat with a recruiter to assess initial fit and introduce the company.

Round 2: Phone Screen (30-45 minutes)
A technical staff member discusses the candidate's experience and technical background, focusing on their strongest areas and project contributions.

Round 3: Data Engineering Code Test (Take-home, ~2 hours estimated completion)
This is a take-home coding challenge, typically released on a Friday with the expectation of questions during the day, though candidates might work on it over the weekend. The company aims for a qualified candidate to complete it within two hours. The challenge is designed to be practical, representing real-world data engineering tasks. An example task involves creating a Lambda function to accept records from Kinesis, convert them to CSV, and write them to S3. Candidates are provided with a skeleton program and unit tests to streamline the setup. The focus is on how the candidate thinks and codes, and their ability to produce relevant work.
After the candidate submits the code, if they move forward, there's an "in-person" (often virtual via Zoom) session to run and debug their code live.

Round 4: Live Debugging & Technical Discussion (Duration Not Specified, part of "Code Test & Mock Consulting Scenario" equivalent)
During this session, the candidate's submitted code is deployed in a dedicated AWS account. The core of this interview is evaluating the candidate's debugging skills. Interviewers intentionally introduce issues (e.g., malformed records in the Kinesis stream) to see how the candidate identifies and resolves problems. This tests their ability to handle real-world data inconsistencies. The candidate is expected to use various approaches to debug, such as checking Redshift's `STL_LOAD_ERRORS` table, reviewing function logs, or comparing record IDs across services.
Following the coding and debugging, SQL skills are assessed by asking the candidate to create progressively difficult queries on the data loaded into Redshift. Discussions also cover broader data engineering topics, including Infrastructure as Code (Terraform, CloudFormation), data validation, schema enforcement, and SRE principles applied to data pipelines.

Round 5: Technical Chat with CTO (30 minutes to 1 hour)
Similar to other technical roles, this round involves a discussion with the CTO about the candidate's technical interests, preferred technologies, and reasoning behind past technical decisions. It helps determine the best fit within the consulting teams.

Overall experience: The process is highly practical, focusing on hands-on coding, debugging, and architectural thinking relevant to data engineering, rather than theoretical algorithms. The company values a candidate's ability to communicate their thought process and approach to real-world problems.