Data Engineering Interview Prep 3-Month 1:1

12 weekly live 1:1 sessions (60 min each). Each session is a mock interview on that week's topic, plus a debrief

A weekly prep plan: exactly what to study and which resources to use before each session (no guesswork, no drowning in 50 tabs)

Honest, detailed feedback after every mock, with specific things to fix

Access to my own study guides across SQL, Spark, streaming, data modeling, ETL design, system design, and the behavioral round

Resume, LinkedIn, and portfolio review built into the program

A final full mock loop + offer and negotiation guidance

The 12-week curriculum

Phase 1 — Foundations & Coding (Weeks 1–4)

Week 1: Kickoff + Diagnostic

• Session: a short diagnostic mock across SQL and concepts to find your gaps, then we build your personalized roadmap.
• Prepare: nothing comes as you are, so we get an honest baseline.

Week 2: SQL Deep Dive

• Session: SQL mock interview (joins, aggregation, window functions, dedup, gap-and-island).
• Prepare: window functions (ROW_NUMBER, RANK, LAG/LEAD, SUM OVER), CTEs, subqueries, NULL handling, query optimization basics.
• Prepare from: my SQL Mastery Guide + daily problems on DataLemur and StrataScratch.

Week 3: Coding / DSA for Data Engineers

• Session: coding mock (easy–medium, the DE bar, not SWE-level).
• Prepare: arrays, strings, hash maps, sets, two-pointer and sliding-window patterns, basic recursion, Big-O.
• Prepare from: NeetCode (arrays/strings/hashmaps tracks) + selected LeetCode easy/medium.

Week 4: Python for Data Engineering

• Session: coding mock with a data-processing flavor + pacing practice.
• Prepare: clean Python, Pandas transforms, file/JSON handling, writing modular pipeline-style code.
• Prepare from: "Python for Data Analysis" (Wes McKinney) + your own practice repo.

Phase 2: Core Data Engineering Depth (Weeks 5–8)

Week 5: Spark Fundamentals

• Session: Spark concepts mock (architecture, DataFrame API, lazy evaluation).
• Prepare: driver/executors, transformations vs actions, DataFrame API, Spark SQL, partitions.
• Prepare from: my Spark Batch/Internals Guide + "Spark: The Definitive Guide" (skim relevant chapters).

Week 6: Spark Internals & Tuning

• Session: Spark deep-dive mock (the round that separates real practitioners).
• Prepare: shuffles, data skew, broadcast vs shuffle joins, partitioning, caching, reading the Spark UI, performance tuning.
• Prepare from: my Spark Internals Guide + Learning Journals (PySpark) + Databricks engineering blog.

Week 7: Data Modeling

• Session: data modeling mock — design a schema for a real business live.
• Prepare: facts vs dimensions, grain, star schema, SCD Type 1 & 2, normalization vs denormalization, star vs Data Vault trade-offs.
• Prepare from: my Data Modeling Bible + Kimball's "The Data Warehouse Toolkit" (key chapters).

Week 8: ETL/ELT & Pipeline Design

• Session: ETL design mock (architecture + trade-offs).
• Prepare: ETL vs ELT, idempotency, incremental vs full loads, orchestration (Airflow), transformations (dbt), data quality, partitioning strategies.
• Prepare from: my ETL Design Framework + scenario dialogues, my Airflow Guide, dbt Learn, "Fundamentals of Data Engineering."

Phase 3: Senior Rounds & Launch (Weeks 9–12)

Week 9: Streaming

• Session: streaming mock (Kafka + Spark Structured Streaming).
• Prepare: topics/partitions/offsets, consumer groups, delivery guarantees, windowing, watermarks, late data, checkpointing, CDC.
• Prepare from: my Kafka Complete Guide + Spark Structured Streaming Guide + Stéphane Maarek's Kafka series + Confluent free training.

Week 10: System Design

• Session: full system design mock (real-time analytics platform, CDC pipeline, or lakehouse migration).
• Prepare: end-to-end design, trade-off reasoning, the lakehouse/Iceberg, the dual-path (speed + truth + reconciliation) pattern, cost and failure handling.
• Prepare from: my System Design Master Guide + "Designing Data-Intensive Applications" (Kleppmann) + Surfalytics DE System Design cheatsheet.

Week 11: Behavioral + Resume/LinkedIn

• Session: behavioral mock (senior framing) + live resume and LinkedIn review.
• Prepare: 8–10 real STAR stories (scope, ownership, conflict, failure, leadership), each quantified.
• Prepare from: my Experience Interview Bible + your own real project history.

Week 12: Full Mock Loop + Launch

• Session: a full mixed mock loop (SQL + design + behavioral), then a portfolio review and offer/negotiation guidance.
• Prepare: your deployed portfolio project ready to present, target company list, and questions on negotiation.
• Prepare from: everything; this is the dress rehearsal and your go-to-market plan.