How to Ace a PySpark Interview at FAANG

PySpark interviews at FAANG companies are notoriously difficult — not because the concepts are impossible, but because most candidates practise the wrong things. After analysing hundreds of real interview reports, there is a clear pattern: companies care about five core areas, and almost every question maps to one of them.

What FAANG Companies Actually Test

Amazon, Meta, Google, and Databricks all have slight variations in style, but the technical content overlaps heavily. At Amazon, you will almost always get a question involving groupBy + aggregation with a business framing ("find the top-selling product per region per month"). At Meta, window functions dominate — especially LAG/LEAD for time-series user behaviour. Google tends to test joins more heavily, particularly left joins and handling nulls. Databricks, being the company behind Apache Spark, goes deepest on optimisation: partitioning, caching, broadcast joins.

1. Transformations and Aggregations

The most common entry-level PySpark question is a grouped aggregation. You will be given a DataFrame and asked to compute something like "total revenue per user per month". The key is to be fluent with groupBy().agg() and know how to use multiple aggregation functions in a single pass.

from pyspark.sql import functions as F

# Classic: revenue per user per month
result = df.groupBy("user_id", F.month("order_date").alias("month")) \
           .agg(
               F.sum("revenue").alias("total_revenue"),
               F.count("order_id").alias("order_count"),
               F.avg("revenue").alias("avg_order_value")
           )

Common mistakes here: using Python built-ins like sum() instead of F.sum(), forgetting to alias columns, and not handling null values before aggregating. Always ask the interviewer whether nulls should be treated as zero or excluded.

2. Window Functions — The #1 Interview Topic

Window functions are the single most tested PySpark topic at senior-level interviews. You need to be completely fluent with rank(), dense_rank(), row_number(), lag(), lead(), and running totals using sum() over a window.

from pyspark.sql.window import Window
from pyspark.sql import functions as F

# Rank users by revenue within each country
w = Window.partitionBy("country").orderBy(F.desc("revenue"))
df = df.withColumn("rank", F.rank().over(w))

# Get previous month's revenue for each user
w2 = Window.partitionBy("user_id").orderBy("month")
df = df.withColumn("prev_revenue", F.lag("revenue", 1).over(w2))

# Running total of revenue per user
w3 = Window.partitionBy("user_id").orderBy("month").rowsBetween(Window.unboundedPreceding, Window.currentRow)
df = df.withColumn("running_total", F.sum("revenue").over(w3))

The question that catches most candidates: "What is the difference between rank() and dense_rank()?" — rank() leaves gaps when there are ties (1, 2, 2, 4), while dense_rank() does not (1, 2, 2, 3). Interviewers use this to test attention to detail.

3. Joins and Handling Nulls

Expect at least one join question. The tricky part is usually not the join itself but what happens with nulls, duplicates, or many-to-many relationships. Always clarify the join key cardinality before writing code.

# Left join — keep all rows from the left DataFrame
result = orders.join(customers, on="customer_id", how="left")

# Broadcast join — use when one table is small (< ~10MB)
from pyspark.sql.functions import broadcast
result = large_df.join(broadcast(small_df), on="id", how="inner")

# Handle nulls after join
result = result.fillna({"customer_name": "Unknown", "revenue": 0})

4. Optimisation Questions

Senior-level interviews always include at least one optimisation scenario. "This job runs for 4 hours, how would you speed it up?" The answer framework: check for data skew, verify partition count matches cluster cores, look for full table scans (add filters early), check if a shuffle join can become a broadcast join, and verify caching strategy for reused DataFrames.

5. How to Structure Your Answer

FAANG interviewers are not just checking if you can write code — they want to see your thought process. Always start by clarifying the schema and asking about edge cases. Think out loud. Write the simplest correct solution first, then optimise. Mention that you would check for nulls, duplicates and skew even if the interviewer does not ask.

The best way to prepare is to solve real PySpark problems with actual code execution — not just reading solutions. Use DataCodingHub to practice the exact question types described in this article.