Simulate a real interview loop
Structured rounds, timed questions, full report at the end.
Senior Data Engineer — Full Loop
Senior Data Engineer
Simulate a complete interview loop for a Senior DE role: phone screen, technical deep dive, system design, and behavioral rounds.
AI/ML Engineer — Full Loop
AI/ML Engineer
End-to-end interview simulation for an AI/ML Engineer: ML fundamentals, LLM & RAG deep dive, system design, and behavioral rounds.
Spark & Pipeline Specialist
Data Engineer (Spark Focus)
Focused drill on Spark internals, PySpark optimization, and ETL pipeline architecture. All medium-hard difficulty.
Quick Practice — 15 min
Any Level
A fast mixed-topic session for when you only have 15 minutes. One question from each type: conceptual, scenario, system design, and behavioral.
AWS Data Engineering
AWS Data Engineer
Deep dive into AWS data services: S3, Glue, EMR, Kinesis, Redshift, and Athena. Covers storage, ETL, streaming, and analytics on AWS.
Snowflake Specialist
Snowflake Data Engineer
Master Snowflake architecture, performance tuning, caching, data sharing, and security. Covers all key topics for Snowflake certifications and interviews.
Databricks Lakehouse
Databricks Data Engineer
End-to-end Databricks coverage: Delta Lake, Unity Catalog, DLT pipelines, MLflow, Photon engine, and Databricks SQL.
Azure Data Engineering
Azure Data Engineer
Azure data platform essentials: ADF pipelines, Synapse Analytics, ADLS Gen2, Event Hubs, and Microsoft Purview.
GCP Data Engineering
GCP Data Engineer
Google Cloud data stack: BigQuery, Dataflow, Pub/Sub, and Dataproc. Covers serverless analytics, streaming pipelines, and batch processing.
Cloud Architecture & Design
Cloud Data Architect
Cross-cloud architecture patterns: data lake design, medallion architecture, data mesh, cost optimization, IAM, and infrastructure as code.
Python for Data Engineering
Data Engineer (Python Focus)
Python skills every data engineer needs: generators, concurrency, memory-efficient processing, pipeline frameworks, and package design.
Kafka & Event Streaming
Streaming Data Engineer
Kafka fundamentals through advanced: core concepts, consumer lag, event sourcing, deduplication, and cross-region replication.
Terraform & IaC
Infrastructure / Platform Engineer
Infrastructure as Code mastery: Terraform state management, modules, providers, drift detection, and multi-cloud provisioning patterns.
Apache Airflow
Data Engineer (Orchestration Focus)
Airflow orchestration deep dive: DAG design, scheduling, operators, XComs, task dependencies, and production deployment patterns.
Orchestration & Workflow
Data / Platform Engineer
Cross-platform orchestration patterns: workflow engines, event-driven pipelines, retry strategies, DAG vs task-based scheduling, and monitoring.
dbt (Data Build Tool)
Analytics Engineer / Data Engineer
dbt essentials: models, tests, materializations, incremental strategies, macros, packages, and analytics engineering best practices.
Python for DE — Deep Dive
Data Engineer (Python Focus)
Comprehensive Python coverage for data engineers: generators, concurrency, PySpark optimization, memory management, packaging, and testing strategies.
Advanced SQL — Deep Dive
Data Engineer (SQL Focus)
Master advanced SQL for data engineering: window functions, recursive CTEs, execution plans, partitioning strategies, SCDs, and engine-specific optimization.
Kafka & Streaming — Deep Dive
Streaming Data Engineer
End-to-end Kafka mastery: architecture, delivery guarantees, partitioning, Schema Registry, Connect, stream processing frameworks, CDC, and advanced patterns.
Data Quality & Observability — Deep Dive
Data Quality Engineer
Comprehensive data quality coverage: dimensions, tools (GX, Soda, dbt), data contracts, lineage, anomaly detection, observability platforms, and organizational adoption.
System Design for DE — Deep Dive
Senior Data Engineer / Data Architect
Data engineering system design: real-time analytics, lakehouses, feature stores, CDC pipelines, data mesh, cost optimization, privacy compliance, and platform design.
LLM Engineer
LLM / AI Engineer
Deep dive into LLM internals: fine-tuning vs. prompting, PEFT methods, prompt injection defense, context window management, cost optimization, and multi-turn systems. Covers the full stack from tokenization to production deployment.
RAG Specialist
AI Engineer (RAG Focus)
End-to-end Retrieval-Augmented Generation: chunking strategies, embedding models, vector search, hybrid search with reranking, RAG evaluation and debugging, and enterprise RAG with access controls.
AI Agents
AI Engineer (Agents Focus)
Building autonomous AI agents: ReAct pattern, tool use and function calling, memory systems, multi-agent coordination, safety sandboxing, planning strategies, observability, and cost control.
ML Eval & Safety
AI Safety / ML Engineer
AI safety, evaluation, and governance: bias detection, fairness metrics, red teaming, hallucination measurement, guardrails architecture, explainability, AI regulation, and responsible deployment checklists.
ML Serving & Infrastructure
ML Platform / MLOps Engineer
Production ML infrastructure: model serving patterns, quantization, GPU optimization, feature stores, deployment strategies, distributed training, self-hosting LLMs, and building ML platforms from scratch.
Behavioral Interview Prep
Any Level
Dedicated behavioral round practice covering the full range: project stories, incident response, stakeholder management, technical decisions, and leadership. Uses STAR method evaluation. Covers Data Engineering, Cloud, Infrastructure, and AI/ML.