Atlassian

Assessment Rubric Overview: "Maximum Profit" Problem

Core Competencies and Skills Evaluated

This problem evaluates a candidate's proficiency in algorithm design, particularly in optimization problems involving sorting and greedy strategies. Candidates are expected to demonstrate a solid understanding of data structures, such as arrays and hash maps, to efficiently manage and compute profits based on item categories and prices. The problem also assesses the ability to analyze and implement algorithms that maximize profit by strategically ordering items for sale.

Behavioral Traits and Problem-Solving Approaches

Interviewers will look for candidates who exhibit structured problem-solving approaches, including:

• Analytical Thinking: Breaking down the problem into manageable components and identifying key factors that influence the outcome.

• Strategic Planning: Formulating a clear plan to solve the problem, considering various constraints and objectives.

• Adaptability: Willingness to adjust strategies based on new insights or constraints that emerge during the problem-solving process.

Additionally, candidates should demonstrate effective communication skills, articulating their thought process and reasoning clearly throughout the interview.

Assessment Process Expectations

Atlassian's interview process typically includes multiple stages:

1. Initial Screening: A recruiter conducts a preliminary interview to assess the candidate's background and fit for the role.

2. Technical Assessment: This may involve coding challenges or problem-solving sessions, either online or in-person, focusing on data structures, algorithms, and system design.

3. System Design Interview: Candidates are asked to design systems or components, demonstrating their ability to architect scalable and efficient solutions.

4. Behavioral Interview: Evaluates alignment with Atlassian's core values and assesses interpersonal skills and cultural fit.

5. Final Interview: Often with the hiring manager, focusing on role-specific questions and final evaluations.

Throughout these stages, interviewers assess both technical expertise and alignment with Atlassian's values.

Preparation Recommendations

To prepare effectively for this type of problem:

• Master Data Structures and Algorithms: Focus on arrays, hash maps, sorting algorithms, and greedy algorithms.

• Practice Problem-Solving: Engage with platforms like LeetCode or HackerRank to solve similar optimization problems.

• Understand System Design: Review concepts related to system architecture, scalability, and efficiency.

• Align with Atlassian's Values: Familiarize yourself with Atlassian's core values and be prepared to discuss how your experiences and approach align with them.

Evaluation Criteria and Technical Concepts

Candidates should demonstrate:

• Algorithmic Efficiency: Ability to design solutions with optimal time and space complexity.

• Problem Decomposition: Skill in breaking down complex problems into simpler sub-problems.

• System Design Acumen: Understanding of designing scalable and maintainable systems.

• Cultural Fit: Alignment with Atlassian's collaborative and innovative culture.

By focusing on these areas, candidates can prepare effectively for the "Maximum Profit" problem and Atlassian's interview process.