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REQUIREMENT_ID_1593 β€’ 3-DAY_ACTIVE_POLICY

Associate Business Development Representative

Company
Company Atlan
Type
Opportunity Type Internship
Salary
Stipend / Salary 6 LPA
Location
Location Remote
Posted Date
Posted Date Oct 06, 2026
Outbound sales prospecting lead qualification CRM (Salesforce) communication AI tools stakeholder mapping negotiation time management adaptability
πŸ“–
Aptitude and Logical Reasoning Practice Open Resource β†—
Helps sharpen analytical thinking and problem‑solving skills, essential for qualifying leads and mapping stakeholder hierarchies.
πŸ“–
Business Development and Sales Fundamentals Open Resource β†—
Provides foundational knowledge of sales processes, outbound strategies, and qualification frameworks useful for a BDR role.
πŸ“–
Interview Preparation and Mock Sessions Open Resource β†—
Offers practice with common interview questions and mock scenarios to build confidence for Atlan’s interview rounds.

Bachelor’s degree in any discipline. No backlogs. Candidates from the 2025, 2026, or 2027 batches are preferred. A passion for sales, AI, and data is essential.

1
Round 1: Phone/Video screening
2
Round 2: Role‑based interview with sales and AI focus
3
Round 3: HR interview
Atlan is a fast‑growing enterprise AI platform that builds the context layer for data teams, enabling AI agents to operate on trusted, governed data. With Gartner and Forrester naming Atlan a Leader across data catalog, governance and metadata management, the company is at the forefront of the AI infrastructure revolution. The mission is simple yet powerful: create a shared context layer that turns raw data into actionable knowledge for AI systems. The Associate Business Development Representative (BDR) role is a full‑time, remote position that places you at the heart of the go‑to‑market engine. You will own pipeline creation within a regional pod in North America, working side‑by‑side with an Account Executive on a shared target. Your day will involve researching accounts, identifying the right stakeholders, crafting highly personalized outreach, and qualifying leads with rigorβ€”confirming pain, need, timeline, and authority before booking meetings. The role is dynamic: you’ll build outbound skills from scratch, work closely with marketing and events, and even experiment with AI workflows to boost efficiency. Key responsibilities: 1. Conduct deep research on target accounts and map stakeholder hierarchies. 2. Develop and execute personalized outreach sequences that drive high reply rates. 3. Qualify leads using a structured framework, ensuring every meeting is high‑value. 4. Collaborate with AEs to hand off qualified prospects and provide feedback on messaging. 5. Maintain accurate records in the CRM, tracking outreach, responses, and next steps. 6. Build and iterate on AI‑powered tools and agents to streamline prospecting. 7. Participate in weekly sales meetings, sharing insights and learning from peers. 8. Track and report on key metrics such as outreach volume, conversion rates, and pipeline velocity. 9. Stay current on industry trends, especially in AI, data governance, and enterprise software. 10. Contribute to the continuous improvement of the sales playbook. Tech stack: You’ll work with Salesforce (or similar CRM), Outreach or Salesloft for sequencing, and AI tools like GPT‑based agents for research and outreach automation. Growth path: After a year, you can move into ABX, GTM Engineering, a player‑coach or team lead role, Customer Success, or Partnerships. The company values internal mobility and fast‑tracked career progression. Why join Atlan? The compensation package is competitive with a strong base, performance variable, and equity. The culture is AI‑native, remote‑first, and trust‑based, offering flexible hours (7β€―PM–4β€―AM IST for NA coverage) and a focus on learning and mastery. Employees enjoy health benefits, flexible time off, and a collaborative environment that encourages experimentation and ownership.

Atlan β€” AI & Machine Learning Interview Guide

Previously asked questions, exam syllabus, coding benchmarks & round strategy.

APTITUDE & LOGIC πŸ“–
Aptitude Practice Questions & Online Mock Tests

Curated logical, quantitative, and verbal reasoning problems to sharpen reasoning skills required for the initial screening test.

Open Resource β†—
COMPANY GUIDE 🎯
Atlan Interview Preparation Corner

Comprehensive guide covering typical interview formats, common questions, and preparation tips for Atlan and off-campus tech roles.

Open Resource β†—
MOCK TESTS & PAPERS πŸ“
Comprehensive Interview Prep Resources & Syllabus

Collection of previous year questions, company-specific test patterns, and interview experiences for technical and HR rounds.

Open Resource β†—
CODING PRACTICE πŸ’»
Algorithm and Data Structure Problem Set

Practice problems to improve coding proficiency and algorithm problem-solving speed for technical rounds.

Open Resource β†—
Round 1: Online Assessment (OA)
Aptitude, Quantitative Logic & 2 Coding Problems
Focus on accuracy and speed. Practice arrays, strings, and standard arithmetic puzzles.
Round 2: Technical Interview 1
Core Tech Stack (Outbound sales, prospecting, lead qualification, CRM (Salesforce), communication, AI tools, stakeholder mapping, negotiation, time management, adaptability) & Live Code Tracing
Explain your thought process aloud. Analyze time & space complexities before coding.
Round 3: System Design & Problem Solving
Database Schemas, APIs & Architecture Basics
Clarify edge cases, diagram schemas cleanly, and discuss scalability trade-offs.
Round 4: HR & Cultural Fit Discussion
Atlan Core Values, Learning Agility & Offer Terms
Demonstrate passion, strong communication, and readiness for full-time collaboration.
What is the bias-variance tradeoff and how do you prevent overfitting? Answer β–Ό
Model Answer: High bias leads to underfitting (oversimplified model), high variance leads to overfitting (captures noise). Mitigate using L1/L2 Regularization, Dropout, Cross-Validation, and data augmentation.
Explain the difference between Precision, Recall, and F1-Score. Answer β–Ό
Model Answer: Precision = TP / (TP + FP) (correctness of positive predictions). Recall = TP / (TP + FN) (coverage of actual positives). F1-Score is the harmonic mean of Precision and Recall.
How does Gradient Descent work and what is the role of Learning Rate? Answer β–Ό
Model Answer: It optimizes loss functions by iteratively moving weights in the direction of negative gradient. A large learning rate may overshoot the minimum; a small rate causes slow convergence.
What is the difference between Supervised, Unsupervised, and Self-Supervised learning? Answer β–Ό
Model Answer: Supervised uses labeled data (X -> y). Unsupervised finds hidden patterns in unlabeled data (clustering/PCA). Self-supervised generates labels from input data (e.g. masked language modeling in BERT/Transformers).
Why do you want to join Atlan as a Associate Business Development Representative?
Preparation Tip: Highlight Atlan's market reputation, recent tech innovations, and how your skills in Outbound sales directly solve their team's objectives.
Describe a challenging bug or academic project roadblock and how you resolved it.
Preparation Tip: Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
How do you handle strict deadlines or sudden scope changes?
Preparation Tip: Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.

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