Warrous is looking for a Senior Database Engineer (Team Lead) in Hyderabad. If Databricks & PySpark are your superpowers, this is your stage.
Skip the boring stuff. The hiring process starts with a real coding challenge—just you, the problem, and a chance to prove you can build enterprise-scale platforms on Azure.
AI. RAG. Massive datasets. This is the good stuff.
Ready to show what you’ve got? Let’s go. 👇
Why Warrous ?
Forget IT support. This is product building.
Warrous builds platforms for automotive and healthcare. You’ll own Databricks pipelines, AI integration, and RAG models.
4–8 years experience? This is your fast-track to Team Lead. Real impact. No boredom.
Ready? 👇.
Senior Data Engineer Hiring Challenge: Overview & Selection Process
The Warrous hiring challenge is designed to simulate the real-world demands of a lead role. It is a confidential, two-round competitive assessment that tests both your coding prowess and architectural thinking.
Challenge Format:
- Round 1: Online Assessment (2 hours)
- 10 MCQs: covering incident management, CI/CD, and database fundamentals.
- 1 Programming Question: Focused on Python/PySpark logic.
- 1 SQL Question: Advanced querying and performance tuning [citation:0].
- Round 2: Project Round
- A comprehensive project question where you will likely design an end-to-end pipeline or debug a complex Databricks workflow, simulating a real-world task you would handle as a Team Lead [citation:0].
Eligibility Criteria and Required Skills for Senior Data Engineers
Eligibility Criteria
| Parameter | Requirement |
|---|---|
| 🧑💼 Experience | 4–8 years in Data Engineering / IT Operations |
| 📍 Location | Hyderabad (Work from office) |
| 🧠 Role Type | Senior Individual Contributor or Team Lead track |
| 🔁 Mindset | Hybrid thinker: Deep engineering + Operational stability |
Skills Required
| Area | Must-Have | Nice-to-Have 🌟 |
|---|---|---|
| 📦 Core Data Stack | Databricks, PySpark, ADLS, PostgreSQL, MSSQL | Delta Lake, Spark SQL, Unity Catalog |
| ⚙️ Ops & Release | Release Management, Production Support, Change Control | CI/CD (Azure DevOps), Incident Management |
| 🤖 AI / Advanced | — | AI Integration, RAG, MCP |
| 🧰 Extras | SQL Tuning, ETL Workflows | Parameterized Notebooks, Job Scheduling |
Senior Data Engineer Roles and Responsibilities
What You’ll Actually Do
- 🏗️ Architect – Design Medallion layers (Bronze, Silver, Gold) with Delta Lake
- ⚡ Optimize – Tune Spark jobs, kill bottlenecks, cut cloud costs
- 🛡️ Stabilize – Own production deploys, monitor quality, fix what breaks
- 🤝 Collaborate – Bridge data scientists → pipelines → AI magic (RAG, ML)
- 👨🏫 Lead – Mentor juniors, review code, set engineering standards
Before vs After: Your Career Upgrade
| 👨💻 Before (Regular Engineer) | 🦸 After (Team Lead at Warrous) |
|---|---|
| You write code | You design the architecture |
| You run queries | You tune Spark for speed & cost |
| You fix your bugs | You own production stability |
| You take tasks | You enable AI use cases |
| You learn alone | You level up the team |
Senior Data Engineer Salary and Benefits
What’s in it for you?
AI Transformation
Build RAG models, not just ETL. Work at AI’s edge.
Ownership
You build it, run it. and scale it. Real product ownership.
Global Impact
Healthcare, automotive—your code reaches millions.
7-Day Preparation Plan for the Data Engineer Hiring Challenge
| Day | Focus | Quick Task |
|---|---|---|
| 1 | 🏗️ Databricks Architecture | Create a parameterized notebook. Understand DAG & Lazy Evaluation. |
| 2 | 🔄 Delta Lake | Practice Time Travel, Vacuum, and Z-Ordering. Use mergeSchema. |
| 3 | 🐍 PySpark | Fix data skew with salting. Master broadcast joins & window functions. |
| 4 | ⚡ Performance Tuning | Debug a slow Spark job. Optimize partitioning. Cut cloud costs. |
| 5 | 🧠 SQL | Solve 5 advanced problems with RANK, LAG, and LEAD. Revise SCD Type 2. |
| 6 | 🚀 CI/CD & Ops | Set up a Databricks CI/CD pipeline on Azure DevOps. Practice incident response. |
| 7 | 🤖 AI + Mock Prep | Learn how RAG uses vector data. Run a full mock interview. |
What 4–8 Years of Data Engineering Experience Looks Like
| Area | What You’ve Probably Done |
|---|---|
| 🏗️ Built | At least 2–3 complete data pipelines from scratch |
| 🔥 Fixed | A production outage at 2 AM. Maybe multiple. |
| ⚡ Optimized | Cut cloud costs by 30%+ with smarter Spark tuning |
| 👨🏫 Mentored | Helped at least 1–2 juniors grow into confident engineers |
| 🗣️ Translated | Explained complex data stuff to non-tech stakeholders |
| 🚀 Shipped | Code that actually reached millions of users |
| 💥 Broke (then fixed) | Something important. And learned forever. |
| 🤖 Experimented | With AI/ML integrations (even if just POCs) |
❌5 Mistakes to Avoid in a Data Engineer Hiring Challenge
- Only talk code → Also explain why you built it that way
- Ignore production → Share how you fixed real outages
- Superficial Spark → Know Databricks deeply (Unity Catalog, clusters)
- Vague stories → Use STAR: Situation, Task, Action, Result
- Skip AI → Learn basics like RAG (takes 10 mins)
Data Engineer Hiring Challenge – FAQs
Q: Is the participation really confidential?
A: Yes, Warrous has marked this challenge as confidential. Your current employer will not be notified of your participation [citation:0].
Q: What is the duration of the challenge?
A: The first round is a 2-hour assessment. The second round is a project-based assessment, the duration of which is typically flexible but requires deep focus [citation:0].
Q: Do I need to know AI/ML to apply?
A: While the core job is data engineering, knowledge of AI Integration and RAG is listed as a “preferred” skill. Understanding how to structure data for AI models will give you a significant edge [citation:0].
Register: Warrous Senior Data Engineer Challenge
How to Register
Final Advice: Think Like a Lead
As you prepare, shift your mindset from “coder” to “Team Lead.” Warrous is not just looking for someone who can write PySpark; they are looking for someone who can architect scalable solutions, guide a team, and ensure business sustainability [citation:0]. In the project round, focus on maintainability, reusability (UDFs, parameterized notebooks), and robustness.
Good luck! Transform this challenge into your next big career breakthrough.