8-Week Data Engineer Mock Interview Practice Plan (2026)

Topics: INNER/LEFT/RIGHT/FULL OUTER JOIN, self-joins, GROUP BY with HAVING, subqueries in WHERE/FROM/SELECT, NULL handling with COALESCE/NULLIF/IS NULL. Daily breakdown: Day 1-2 JOINs (5 problems). Day 3-4 GROUP BY and aggregation (5 problems). Day 5-7 subqueries and combined patterns (5 problems). Run every query on DataDriven. End-of-week milestones: Solve any JOIN problem in under 10 minutes. Write GROUP BY with HAVING from memory without syntax errors. Handle NULLs in JOINs and aggregations correctly on first attempt.

Topics: ROW_NUMBER/RANK/DENSE_RANK, LAG/LEAD for sequential comparisons, running totals with SUM OVER, moving averages with AVG OVER, ROWS BETWEEN vs RANGE BETWEEN, recursive CTEs, multi-level CTEs. Daily breakdown: Day 1-2 ranking functions (4 problems). Day 3-4 LAG/LEAD and running calculations (5 problems). Day 5 recursive CTEs (3 problems). Day 6-7 mixed window function problems (3 problems). End-of-week milestones: Write ROW_NUMBER/RANK/DENSE_RANK with correct PARTITION BY and ORDER BY on first attempt. Use LAG/LEAD for period-over-period comparisons. Write a recursive CTE for hierarchical data traversal.

Topics: JSON parsing and nested traversal, CSV processing with generators, file I/O and memory-efficient reading, collections (Counter, defaultdict, OrderedDict, deque), dictionary comprehensions, error handling with specific exceptions. Daily breakdown: Day 1-2 JSON parsing and flattening (3 problems). Day 3-4 file processing with generators (3 problems). Day 5-7 collections and mixed patterns (4 problems). End-of-week milestones: Flatten nested JSON of any depth without looking up the approach. Process a large file using generators without loading it into memory. Use collections.Counter, defaultdict, and deque fluently.

Topics: Sessionization with inactivity gaps, retry logic with exponential backoff and jitter, schema validation and dead letter queues, hash-based change detection, decorators for logging and timing, type hints and dataclasses. Daily breakdown: Day 1-2 sessionization and time-window logic (3 problems). Day 3-4 retry and error handling patterns (3 problems). Day 5-7 validation, change detection, and production patterns (4 problems). End-of-week milestones: Implement sessionization from scratch in under 15 minutes. Write retry with exponential backoff and jitter from memory. Build a schema validation function that routes bad records to a DLQ.

Topics: Star schema design (grain, facts, dimensions), dimension types (conformed, degenerate, junk, role-playing), SCD Types 1/2/3, data vault (hubs, links, satellites), medallion architecture, OBT vs star schema trade-offs, bridge tables for many-to-many. Daily breakdown: Day 1-2 star schema design for e-commerce and ride-sharing (3 exercises). Day 3-4 SCD handling and history tracking (3 exercises). Day 5-6 data vault and medallion architecture (2 exercises). Day 7 mixed modeling with trade-off discussions (2 exercises). End-of-week milestones: Design a star schema for any domain in under 20 minutes with correct grain, facts, and dimensions. Explain SCD Types 1, 2, and 3 with concrete examples and trade-offs. Articulate when to use star schema vs data vault vs OBT.

Topics: Ingestion patterns (batch, micro-batch, streaming), message queues (Kafka, SQS, Pub/Sub), orchestration (Airflow, Dagster, Prefect), idempotency strategies (DELETE-INSERT, MERGE/UPSERT, atomic swap), schema evolution, monitoring (freshness, volume anomalies, schema drift), failure handling (retries, DLQ, circuit breakers), pipeline optimization. Daily breakdown: Day 1-2 clickstream pipeline and multi-source API ingestion (2 designs). Day 3-4 migration planning and optimization (2 designs). Day 5-6 monitoring and failure handling (2 designs). Day 7 mixed architecture designs (2 designs). End-of-week milestones: Design an end-to-end pipeline in a structured 15-minute walkthrough. Explain batch vs streaming trade-offs with specific latency, cost, and complexity numbers. Describe idempotency strategies and when to use each.

Topics: DataFrame API (select, filter, groupBy, agg, join), window functions in PySpark, join strategies (broadcast, sort-merge, shuffle hash), repartition vs coalesce, data skew detection and salting, caching and persistence, Delta Lake (MERGE, OPTIMIZE, time travel), Structured Streaming (watermarks, output modes, triggers). Daily breakdown: Day 1-2 DataFrame transformations and window functions (3 problems). Day 3-4 join optimization and skew handling (3 problems). Day 5-6 Delta Lake operations (2 problems). Day 7 Structured Streaming (2 problems). End-of-week milestones: Write a PySpark transformation with correct partitioning and no unnecessary shuffles. Debug a Spark OOM error by identifying skew vs insufficient memory. Explain the Catalyst optimizer stages and what each one does.

Schedule: Monday and Thursday are full mock interview loops (4 rounds each). Tuesday and Friday are timed individual rounds on weak areas. Wednesday and Saturday are for reviewing mistakes from mock interviews. Sunday: rest. Mock loop structure: Round 1 SQL (45 min), 15-min break, Round 2 Python (45 min), 15-min break, Round 3 System Design (45 min), 15-min break, Round 4 Behavioral (30 min). Use DataDriven's mock interview simulator to automate question selection, timing, and AI feedback. End-of-week milestones: Complete a full mock interview loop in under 3 hours. Solve Medium SQL and Python problems in under 15 minutes each while explaining your approach. Deliver a structured system design walkthrough in 30 minutes.