Here are the stages I went through, plus what I gathered from others online:
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𝟭. 𝗥𝗲𝘀𝘂𝗺𝗲 𝗦𝗵𝗼𝗿𝘁𝗹𝗶𝘀𝘁𝗶𝗻𝗴 / 𝗥𝗲𝗰𝗿𝘂𝗶𝘁𝗲𝗿 𝗦𝗰𝗿𝗲𝗲𝗻 The recruiter reviews your resume to check alignment with technical skills (SQL, data pipelines, cloud tools like Azure, Spark etc.) and project experience. They may also ask about your background, motivation, and career goals. Strong communication and a clear resume really help.
𝟮. 𝗧𝘄𝗼 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗥𝗼𝘂𝗻𝗱𝘀 (1 Hour Each) These rounds dive deep into technical expertise. Common topics: • SQL performance & optimization • Data modelling • Pipeline & ETL design • Handling edge cases • Cloud services (Azure Data Factory, Databricks, Synapse) • DSA questions on Arrays & Linked Lists, Queue
One round may involve system/architecture design (e.g., scalable data warehouse, streaming pipeline). Another may focus on coding or troubleshooting data pipelines.
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𝟯. 𝗛𝗶𝗿𝗶𝗻𝗴 𝗠𝗮𝗻𝗮𝗴𝗲𝗿 𝗥𝗼𝘂𝗻𝗱 This round mixes technical and behavioural aspects. The manager checks for: • Problem-solving ability • Ownership • Stakeholder management
𝟰. 𝗔𝗔 (𝗔𝘀 𝗔𝗽𝗽𝗿𝗼𝗽𝗿𝗶𝗮𝘁𝗲) / 𝗠𝗮𝗻𝗮𝗴𝗲𝗿𝗶𝗮𝗹 𝗥𝗼𝘂𝗻𝗱 A senior-level evaluation focusing on leadership, collaboration, and cultural fit. You may face behavioural questions about handling ambiguity, conflict, mentoring, and driving impact across teams. They may also ask how you ensure scalability, quality, and reliability in data systems.
𝟱. 𝗛𝗥 𝗥𝗼𝘂𝗻𝗱: 𝗦𝗮𝗹𝗮𝗿𝘆 & 𝗢𝗳𝗳𝗲𝗿 𝗗𝗶𝘀𝗰𝘂𝘀𝘀𝗶𝗼𝗻 Covers compensation (base, bonus, stocks), benefits, role level, and formalities like relocation or background checks.
Big News in AI! You can now run 100B parameter models on your local CPU – no GPU needed!
Microsoft has open-sourced their lightning-fast 1-bit LLM inference framework: bitnet.cpp
Here’s why it’s a game-changer: ⚡ 6.17x faster inference ♻️ 82.2% less energy consumption on CPUs 🤖 Supports top-tier models like LLaMA 3, Falcon 3, and BitNet
Run huge models locally, efficiently, and open-source! Welcome to the new era of AI inference.
