Enqurious Helped a Billion-Dollar Enterprise AI Company Improve Debugging Skills and Achieve 70% Faster Bug Resolution with Bug Bounty Program

A $700M Enterprise AI Company sought to build a talent pool of industry-ready Databricks and Snowflake Data Engineers. The challenge was to develop a learning experience that went beyond certification exams, providing candidates with practical, real-world project experience. Enqurious partnered with the client to create a hands-on, scenario-driven learning path that increased certification clearance rates and equipped learners with real-world skills.

Problem Statement

The AI company had two primary objectives:

1. Certification Clearance Rate: The goal was to have learners achieve a high clearance rate for both Databricks and Snowflake certifications.
   

2. Real-World Project Exposure: The aim was to equip candidates with practical, industry-relevant skills, moving beyond just passing exams to becoming job-ready engineers. This practical experience was key to the client’s vision of developing capable data engineers who could seamlessly integrate into real-world business projects.

The challenge was to design an immersive, hands-on learning experience that not only prepared learners for certification but also provided them with the skills required for real-world data engineering tasks.

How We Helped

Enqurious designed a 4-week, scenario-driven, lab-integrated skill path tailored specifically to the client’s needs. The learning path included:

• Masterclasses: Focused on foundational concepts in Databricks and Snowflake, building strong industry-specific knowledge for learners.

• Lab-Integrated Scenarios: Seamlessly integrated sandboxes with industry-inspired scenarios. These scenarios covered the full scope of real-world data engineering tasks, including architecture design, data ingestion, and complex workflows in the E-commerce domain. The scenarios were crafted to replicate challenges learners would face in actual job roles, ensuring they gained hands-on experience.

• Mock Tests: Full-length mock tests designed to closely mirror the actual certification exams. These were structured to familiarize learners with the exam format and provide a realistic testing environment to assess their readiness.

Metrics & Value Proposition

• Early Warning System: A proactive approach to learner engagement, identifying candidates who might need additional support. This early intervention system ensures timely actions are taken to boost learner success.

• Skill Intelligence: In-depth insights into learners' skill development, enabling focused improvement in areas that need the most attention. By honing in on these targeted areas, learners can optimize their study time, avoid unnecessary broad mock tests, and enhance their efficiency.

• Learning Progress Tracking: Continuous monitoring of learner progression to ensure they remain on track. This feature helped both learners and instructors keep a clear overview of development.

• Automated Recommendation List: A system that provides personalized recommendations based on the learner's progress. This feature acts as a guide for learners, offering suggestions on when they are ready to sit for the certification exams, helping them maximize their time and efforts effectively.

The Impact Experienced

• Certification Success:
  

  • 97% clearance rate for Databricks Associate Data Engineer Certification, with 400 learners successfully passing.

  • 92% clearance rate for SnowPro Core Certification, with 350 learners successfully passing.

• Increased Business Value:

  • 20% increase in differential billing due to the enhanced capabilities of the certified learners.

A billion-dollar Enterprise AI Company, faced significant productivity challenges in their data engineering projects due to the lack of effective debugging skills among their Junior Data Engineers. This issue resulted in long periods of time spent on bug identification, resolution, and testing, impacting the overall efficiency of their teams.

Problem Statement

• Challenges:
  

  • Time-Intensive Debugging: Nearly 75% of active work time was consumed by debugging processes.

  • Inadequate Knowledge: Junior Data Engineers were unfamiliar with common issues in key areas such as data ingestion, quality treatment, storage, ETL, and data modeling.

  • Impact on Productivity: Due to the lack of awareness of frequently occurring issues, engineers spent excessive time figuring out problems rather than focusing on system design, implementation, and problem-solving.

• Goals:
  

  • Reduce Debugging Time: Shift focus from bug resolution to more productive activities like system design and problem-solving.

  • Increase Debugging Awareness: Equip engineers with the knowledge of the 20% of issues that cause 80% of the bugs in SQL data wrangling workflows.

How We Helped

Enqurious designed a Bug Bounty program aimed at improving the debugging skills of Junior Data Engineers by simulating real-world issues they commonly face.

• Program Design:
  

  • The program consisted of 10 scenarios, each with buggy code covering various intermediate to advanced SQL topics including CTEs, Window functions, Data Modeling, and Performance Tuning.

  • Error Types: The scenarios ranged from simple syntax errors to complex logical flaws and poor code design.

  • Engagement: To foster competition and engagement, a leaderboard was introduced to highlight the engineers who identified and fixed the most bugs.

• Additional Features:

  • Leaderboard to track and reward top engineers based on points earned.

  • Progress Tracking to monitor individual learning journeys.

  • Skills Assessment to identify engineers with great debugging skills and those who needed further development.

  • Data-Driven Insights on acquired skills and areas requiring attention.

Metrics & Value Proposition

• Participation: 200+ Junior Data Engineers participated in the Bug Bounty.

• Completion Rate: Over 90% of the engineers successfully completed the program.

• Skill Improvement:

  • 70% of participants reported a significant improvement in their debugging skills.

  • Faster identification and resolution of bugs, leading to better testing and documentation of workflows.

• Value Delivered:

  • Time Reduction: A noticeable reduction in time spent on debugging, allowing engineers to dedicate more time to system design, implementation, and problem-solving.

  • Increased Confidence: Engineers gained confidence in troubleshooting common issues, improving their efficiency in real-world projects.

  • Skill Tracking: Data-driven insights enabled the identification of engineers’ strengths and weaknesses, allowing for tailored skill development plans.

The Impact Experienced

The Bug Bounty program yielded significant improvements in both technical skills and overall productivity:

• Enhanced Debugging Skills: Engineers became more proficient in identifying common bugs and resolving issues quickly, improving workflow efficiency.

• Faster Time to Resolution: With improved debugging knowledge, engineers reduced the time spent on bug identification and resolution, contributing to increased project delivery speed.

• Better Documentation and Testing: Engineers also developed a stronger focus on documenting bugs and testing solutions more thoroughly, ensuring higher-quality data workflows.