Module 7SQL and dataBoth kinds of role

SQL and data for FDEs

Where SQL and data work show up in loops, dataframe and applied data science rounds, window functions, cohorts and funnels, query plans and indexes, slowly changing dimensions, pipelines and backfills, and the Spark basics that data-platform roles assume.

8 lessons
1 h 28 min
All in Pro
8 of 8 live
Both kinds of role

By the end of this module you can

Merge datasets in a notebook and prove every join with row counts
Write window functions, cohorts and funnels without a fan-out
Read a query plan and say why a query is slow
Design a backfill that is safe to run twice

Where this round is reported

Databricks publishedDatabricks’ US senior FDE postings ask for deep Apache Spark experience, including knowledge of Spark runtime internals. Source 1Sr. Forward Deployed Engineer (FDE) - Financial Services (New York City)PublisherDatabricks (careers site / Greenhouse)Source typecompany job posting
Databricks one candidate reportOne candidate for Databricks’ AI FDE role reported, in August 2026 and before interviewing, being told that the coding round focuses on applied data science and traditional machine learning, and wrote that it is the only coding round in the process. Source 2Anyone been through interviews for AI FDE at Databricks ? (post by u/Haunting_Ad3263)PublisherReddit r/cscareerquestionsukSource typecandidate report on RedditSource 3Anyone been through interviews for AI FDE at Databricks ? (comment by u/Haunting_Ad3263)PublisherReddit r/cscareerquestionsukSource typecandidate report on Reddit
Salesforce one candidate reportOne commenter in a Reddit thread about Salesforce’s Agentforce FDE role reported, in August 2026, an assessment that included basic SQL alongside a simple coding task. Source 4Forward Deployed Engineer (Agentforce) (comment by u/joemons)PublisherReddit r/SalesforceCareersSource typecandidate report on Reddit

No employer publishes a pass rate or a round weight.

The lessons

8 lessons, 1 h 28 min of reading. 8 of 8 live today.

7.1Where SQL and data work show up in FDE loops10 minProLive

You can say where SQL and data-platform skills appear in your targets’ postings and reported assessments, and pick drills to match.

What it covers

Four strands of data work
SQL in assessments
SQL, modeling and pipelines in postings
Spark and data-platform roles
Data rounds described before they happen
What to prepare for your targets

Key takeaways

Candidates report SQL questions in online assessments at more than one company; none of them describes the query.
Postings list SQL as one language among several, or data modeling and ETL as requirements or a bonus; Databricks’ senior FDE postings ask for Spark runtime internals.
Three data rounds were described only before anyone sat them, so prepare the moves they share: say what one row means, count rows in and out of each step, and tie the total before you give it.
Make a left join that keeps zeros automatic before anything else; every strand in this module builds on it.

Practice

2 more planned.

Do this now

Answer the join fan-out question cold, then use your misses to pick where to start

7.2Dataframe and applied data science rounds11 minProLive

Given a few datasets in a notebook, you can merge them correctly, answer the questions asked, make one clear chart, and say what the data cannot tell you, inside an hour.

What it covers

What employers and candidates describe
Checking a dataset in the first five minutes
Joins, and the row counts that prove them
Group-by answers to business questions
One chart that answers the question
A simple model when asked, and how to judge it
What the data cannot tell you

Key takeaways

Databricks’ Sr. Deployment Strategist, FDE postings ask for someone who can explore data and work in notebooks.
One Databricks AI FDE candidate was told, before the round, that the coding round focuses on applied data science and traditional machine learning, and wrote that it is the only coding round; one Reddit commenter described an hour of merging datasets and making charts.
Prove every join with row counts before you answer from it.
Say what the data cannot tell you; it is part of the answer, not an apology.

Practice

4 more planned.

Do this now

Answer the baseline churn model question in a notebook: row counts after every merge, one chart, two limits

7.3Window functions and CTEs12 minProLive

You can write ranking, running and offset window queries, explain partitions, ordering and frames, and build a date spine with a recursive CTE.

What it covers

Partitions and ordering
Ranking functions
Running aggregates
Offsets: lag and lead
Frames: rows versus range
CTEs and the recursive date spine
Common mistakes

Key takeaways

A window function keeps every row; a group by collapses them.
Always state the ordering and the frame.
Ties change ranking results; choose the function on purpose.
Test with duplicates and nulls.

Practice

10 more planned.

Do this now

Write the running total cold, then show every calendar day and explain the frame

7.4Cohorts, rates and funnels11 minProLive

You can compute a cohort return rate, a retention curve and a funnel conversion without double counting.

What it covers

Defining the cohort
Numerators and denominators
Retention curves
Funnels and ordering of steps
Double counting and fan-out
Before you show a rate

Key takeaways

Write the denominator first.
Cohorts need a fixed entry event and a fixed window.
Funnels must respect step order and count each user once.
Check totals against a simple count.

Practice

14 more planned.

Do this now

Say the cohort and the denominator, then compute the return rate by cohort

7.5Reading query plans; indexes and partitions11 minProLive

You can read a query plan, find the scan that hurts, propose an index and state its cost on writes.

What it covers

Reading a plan
Scans, seeks and joins
Choosing an index
Covering and composite indexes
Partitioning
What changes at scale

Key takeaways

Find the step that touches the most rows.
Index for the filter and the join, then for the sort.
Every index costs writes and storage.
Partition large tables by the column most queries filter on and the data expires by; small tables don’t need it.

Practice

This query got slow after the table grew. Read the plan and say what you would change.

7 more planned.

Do this now

Read the slow query’s plan, name the node, then propose one index and its write cost

7.6Slowly changing dimensions, gaps and islands, deduplication11 minProLive

You can model history with a slowly changing dimension, find continuous runs in event data, and deduplicate by rule.

What it covers

History you can query as of a date
Gaps and islands
Deduplication rules
Sessionization
Testing these patterns
Put it together

Key takeaways

Store validity ranges, not just the current value.
Find runs with a difference of two row numbers, or with a change flag and a running sum.
Deduplication needs an explicit tie-break rule.
Sessionize with a lag and a running sum.

Practice

11 more planned.

Do this now

Return each ticket’s latest status with a stated tie-break, then its status as of last month

7.7Pipelines: dbt and Airflow mental models, backfills, idempotency11 minProLive

You can structure a pipeline into layers, make each run idempotent and backfill a year without double counting.

What it covers

Layers: staging, intermediate, marts
Idempotent partitions
Backfills
Late-arriving data
Tests on data
Ownership

Key takeaways

Rebuild a partition, never append to it.
Backfills must use the same code path as daily runs.
Data tests catch broken feeds before customers do.
Every model needs an owner.

Practice

Your résumé mentions a data pipeline you built. Walk me through its failure modes and how you monitored it. Pro

11 more planned.

Do this now

Run the migration case and prove month-end still ties: parallel runs and a reconciliation per partition

7.8Spark basics for data-platform loops11 minProLive

You can explain partitions, shuffles and skew, and rewrite a slow join, in terms a data-platform interviewer expects.

What it covers

The execution model
Shuffles
Skew and how to fix it
DataFrames versus SQL
Reading the UI
Answering the skew question out loud

Key takeaways

Wide transformations shuffle rows between partitions, and on a cluster between machines; when a job is slow, find the shuffle first.
Fix skew in order: bad keys, adaptive execution, a broadcast, then isolate or salt only the hot key.
SQL and DataFrame code compile to the same plan; choose DataFrames where you want small functions you can test.
Answer the skew question in four steps: the symptom, the key, the fix and the check.

Practice

Group page views into sessions separated by a period of inactivity.

2 more planned.

Do this now

Answer the sessionize question as if on Spark: the exchange, the hot user, the fix

Preview a Pro lesson

Lesson 7.110 minProLive

Where SQL and data work show up in FDE loops

You can say where SQL and data-platform skills appear in your targets’ postings and reported assessments, and pick drills to match.

What it covers:

Four strands of data work
SQL in assessments
SQL, modeling and pipelines in postings
Spark and data-platform roles
Data rounds described before they happen
What to prepare for your targets

What FDE coding rounds test gave the data round a single row; this module opens it up. A customer will act on your numbers, so the bar is a number you can defend, not a query that runs. By the end you can write a “Data:” line for each target and, cold, the query every strand below builds on: a left join that keeps zeros.

Four strands of data work

Name the strand your target’s evidence points to, then start with its first drill.

SQL in an online assessment. A query or two in a screening test. Drill running total, then return-rate cohort; study window functions.
Modeling and pipelines. History, duplicates and nightly loads. Drill latest status per ticket as a dedup rule, then sessionize; study history and dedup and pipelines.
A notebook round. A few datasets and questions, sometimes a model. Drill messy CSV; study dataframe rounds.
Data-platform work. Spark is the job, and speed is part of being right. Drill slow query; study query plans, then Spark basics.

Pick your strand from the posting you applied to, not from the company’s other roles: at Databricks, below, a senior posting and an AI FDE posting point to different strands.

Practice

Cases

Moving analytics to a new data platformPro

Questions

Design prompts

2 planned

Coding drills

2 planned

SQL drills

22 planned

Take-homes

1 planned

Locked behind Pro

Every lesson in Module 7 is in Pro, the first lesson included, with its preview and practice.

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Do these first. Each is live and free.

The evidence behind this module

The plan for this module cites 20 claims: 10 from employers' own pages and postings and 10 candidate reports. How we source what we say.

What Pro adds to this module today

8 of 8 Pro lessons live, and the rest as they ship.
Model answers to 4 questions this module practices Pro

See Pro and pricing

Questions about Module 7

Is Module 7 free?

No. Every lesson in Module 7 is in Pro, the first lesson included.

How long is Module 7?

8 lessons, 88 minutes of reading in all, plus the practice each lesson ends with.

Is Module 7 written yet?

Yes. All 8 lessons are live.

Which kinds of FDE role is it for?

Both: roles at AI labs and AI product companies, and roles at enterprise software and data companies.

Sources

Sr. Forward Deployed Engineer (FDE) - Financial Services (New York City) Databricks (careers site / Greenhouse), company job posting
Anyone been through interviews for AI FDE at Databricks ? (post by u/Haunting_Ad3263) Reddit r/cscareerquestionsuk, candidate report on Reddit
Anyone been through interviews for AI FDE at Databricks ? (comment by u/Haunting_Ad3263) Reddit r/cscareerquestionsuk, candidate report on Reddit
Forward Deployed Engineer (Agentforce) (comment by u/joemons) Reddit r/SalesforceCareers, candidate report on Reddit