Analyzing 89 Responses to a SQL Screener Question for a Senior Data Analyst Position

At Help Scout, we recently went through the process of hiring a new senior data analyst. In order to apply for the position, we asked anyone interested to answer a few short screener questions including one to help evaluate their SQL skills.

Here’s the SQL screener question we asked. If you’re an analyst, you’ll get the most value out of this post if you work through it before reading on:

We’re currently in the process of rolling out Beacon, our live chat tool, to existing customers who have expressed interest trying it.

Customers could have expressed interest in two ways: either by filling out an interest form or mentioning to our support team that they want to try it.

For the interest form, there is one table, hubspot.contact, with two relevant fields:

email – The user’s email address
property_beacon_interest – A Unix timestamp in milliseconds representing when they filled out the form or null if they have not expressed interest

+------------------+--------------------------+
|      email       | property_beacon_interest |
+------------------+--------------------------+
| matt@example.com |            1534101377000 |
| eli@example.com  |                          |
+------------------+--------------------------+

When a customer expresses interest in a support conversation, our support team tags the conversation with a beacon-interest tag. There are two relevant tables:

helpscout.conversation with three relevant fields:

id – The id of the conversation
email – The email of the person who reached out to support
created_at – A timestamp with the date/time the conversation was created

+----+-------------------+--------------------------+
| id |       email       |        created_at        |
+----+-------------------+--------------------------+
|  1 | matt@example.com  | 2018-08-14 14:02:10 UTC  |
|  2 | eli@example.com   | 2018-08-14 14:06:30 UTC  |
|  3 | matt@example.com  | 2018-08-14 14:07:33 UTC  |
|  4 | katia@example.com | 2018-08-14 14:11:30 UTC  |
|  5 | jen@example.com   | 2018-08-13 14:11:30 UTC  |
+----+-------------------+--------------------------+

There’s also a helpscout.conversation_tag table with two relevant fields:

conversation_id – The id of the conversation that was tagged. A conversation can have zero or more tags.
tag – The name of the tag

+-----------------+-----------------+
| conversation_id |       tag       |
+-----------------+-----------------+
|               1 | new-trial       |
|               1 | bug-report      |
|               2 | beacon-interest |
|               4 | beacon-interest |
+-----------------+-----------------+

Your challenge:

Write a SQL query (any dialect is fine) that combines data from these two sources that lists everyone who has expressed interest in trying Beacon and when they first expressed that interest.

The end result using the example tables above should be a functioning SQL query that returns the following:

+-------------------+-------------------------+
|      email        |  expressed_interest_at  |
+-------------------+-------------------------+
| matt@example.com  | 2018-08-12 19:16:17 UTC |
| eli@example.com   | 2018-08-14 14:06:30 UTC |
| katia@example.com | 2018-08-14 14:11:30 UTC |
+-------------------+-------------------------+

You should include your query in the response field to this question in the online application.

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screen.md

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This question – designed to be answered in 10-15 minutes – proved incredibly valuable because it served as an easy way to evaluate whether applicants had the minimum technical skills to be effective in this role. This position requires working a lot with SQL, so if an applicant struggled with an intermediate SQL challenge, they would likely struggle in the role as well.

What surprised me is that almost none of the answers were identical, except for a few towards the end because someone commented on the Gist with a (slightly buggy) solution :).

For reference, here’s how I would answer the question:

	with hubspot_interest as (

	select
	email,
	timestamp_millis(property_beacon_interest) as expressed_interest_at
	from hubspot.contact
	where property_beacon_interest is not null

	),

	support_interest as (

	select
	email,
	created_at as expressed_interest_at
	from helpscout.conversation
	join helpscout.conversation_tag on conversation.id = conversation_tag.conversation_id
	where tag = "beacon-interest"

	),

	combined_interest as (

	select * from hubspot_interest
	union all
	select * from support_interest

	),

	final as (

	select
	email,
	min(expressed_interest_at) as expressed_interest_at
	from combined_interest
	group by 1

	)

	select * from final

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sql-answer.sql

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There are a lot of variations on this though that will still result in the correct answer – and many which won’t. For example, no points lost for using uppercase SQL instead of lowercase. But if the query doesn’t union the tables together at some point, it probably wouldn’t result in the correct answer.

If you’re interested in data analysis and analytics, you can subscribe to my Matt on Analytics newsletter to get notified about future posts like this one.

Analyzing the Differences

It would be impossible to list every difference – as you’ll see at the end of this post in the anonymized responses, there are and endless number of ways to format the query.

That said, there are a lot of common differences, some substantial, some not.

SQL Casing

Does the query use uppercase or lowercase SQL or some combination of the two?

Note that in these examples and all that follow, the answers aren’t necessarily correct. They’re just chosen to highlight different ways of approaching the query.

	# Good: All uppercase

	SELECT
	email,
	datetime(property_beacon_interest/1000, 'unixepoch') AS expressed_interest_at
	FROM
	hubspot.contact
	WHERE
	property_beacon_interest IS NOT NULL
	UNION
	SELECT
	c.email,
	c.created_at AS expressed_interest_at
	FROM
	helpscout.conversation c
	INNER JOIN helpscout.conversation_tag ct
	ON c.id = ct.conversation_id AND ct.tag = 'beacon-interest'


	# Good: All lowercase

	select
	email,
	first_interest = min(first_interest)
	from (
	— interest forms
	select
	email,
	first_interest = dateadd(S, property_beacon_interest/1000, '1970-01-01')
	from
	hubspot.contact
	where
	property_beacon_interest is not null

	— support team tags
	union all
	select
	email,
	first_interest = created_at
	from
	helpscout.conversation c join
	helpscout.conversation_tag ct on c.id = ct.conversation_id and ct.tag = 'beacon-interest'
	) combined
	group by
	email

	# Okay: Mixed uppercase and lowercase

	SELECT helpscout.conversation.email, helpscout.conversation.created_at as expressed_interest_at
	FROM helpscout.conversation
	INNER JOIN helpscout.conversation_tag ON helpscout.conversation.id=helpscout.conversation_tag.conversation_id
	WHERE tag="beacon-interest"
	UNION
	select hubspot.contact.email, DATETIME(hubspot.contact.property_beacon_interest/1000, 'unixepoch') \|\| ' UTC' as expressed_interest_at
	FROM hubspot.contact
	WHERE property_beacon_interest != ''
	ORDER BY expressed_interest_at;

	# Bad: Other variations

	Select
	email
	From
	helpscout.conversation
	Where
	created_at
	And
	conversation_id
	Order By
	email

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casing.sql

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Common Table Expressions (CTEs) vs Subqueries

Common Table Expressions go a long way towards making your query easy to read and debug. Not all SQL dialects support CTEs (MySQL doesn’t, for example), but using them in the query was almost always an indicator of an experienced analyst.

	# GOOD: CTEs
	# I'd put each CTE on a new line though to make it easier to comment and copy/paste

	with form as (

	select
	email,
	to_timestamp(property_beacon_interest) as expressed_interest_at
	from hubspot.contact

	), support as (

	select
	c.email,
	c.created_at as expressed_interest_at
	from hubspot.conversation c
	left join hubspot.conversation_tag t on c.id = t.conversation_id
	where t.tag = 'beacon-interest'

	), unioned as (

	select * from form
	union all
	select * from support

	), final as (

	select
	email,
	min(expressed_interest_at) as expressed_interest_at
	from unioned
	group by 1
	order by 2

	)

	select * from final

	# OKAY: Subqueries

	SELECT email, MIN(created_at) AS expressed_interest_at
	FROM
	(
	SELECT a.email, MIN(created_at) AS created_at
	FROM conversation a
	INNER JOIN conversation_tag b
	ON a.id=b.conversation_id
	WHERE b.tag="beacon-interest"
	GROUP BY id
	UNION
	SELECT email,DATETIME(time_created/1000,'unixepoch')\|\| ' UTC' AS created_at
	FROM hubspot.contact
	WHERE time_created IS NOT NULL
	)
	GROUP BY email ORDER BY expressed_interest_at

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ctes.sql

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Meaningful CTE Names

CTEs benefit a lot from meaningful names that make it easy for you and other analysts to interpret.

	# Good: Descriptive names

	WITH beacon_interest_contact as (
	SELECT
	email,
	FROM_UNIXTIME(property_beacon_interest/1000) as expressed_interest_at
	FROM
	hubspot.contact
	WHERE
	property_beacon_interest IS NOT NULL),
	beacon_interest_conversation as (
	SELECT
	email,
	created_at as expressed_interest_at
	FROM
	helpscout.conversation
	INNER JOIN
	helpscout.conversation_tag
	ON
	conversation.id = conversation_tag.conversation_id
	WHERE
	conversation_tag.tag = 'beacon-interest'),
	beacon_interest_union as (
	SELECT
	*
	FROM
	beacon_interest_contact
	UNION
	SELECT
	*
	FROM
	beacon_interest_conversation)
	SELECT
	email,
	MIN(expressed_interest_at) as expressed_interest_at
	FROM
	beacon_interest_union
	GROUP BY
	email;

	# Bad: Non-descriptive names

	;with x0 as
	(
	Select hcv.email, hcv.created_at expressed_interest_at
	From helpscout.conversation hcv
	Inner join helpscout.conversation_tag hct on hcv.id = hct.conversation_id
	Where hct.tag = ‘beacon–interest’
	Union
	Select email, dateadd(S,property_beacon_interest, '1970-01-01') expressed_interest_at
	From hubspot.contact
	Where property_beacon_interest is not null

	),

	X1 as (
	Select email , expressed_interest_at, row_number() over (partition by email order by expressed_interest_at) as RowNo
	From x0
	Order by expressed_interest_at
	)

	Select email, expressed_interest_at
	From x1
	Where RowNo =1

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cte-names.sql

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INNER JOIN vs LEFT JOIN

Either works, but INNER JOIN performs better and is more intuitive here.

	# Good: INNER JOIN

	…
	SELECT a.email AS email, a.created_at AS expressed_interest_at
	FROM helpscout.conversation AS a
	INNER JOIN helpscout.conversation AS b ON a.id = b.conversation_id
	WHERE b.conversation_id = 'beacon-interest'
	…

	# Okay: LEFT JOIN

	…
	select
	c.email,
	c.created_at as expressed_interest_at
	from hubspot.conversation c
	left join hubspot.conversation_tag t on c.id = t.conversation_id
	where t.tag = 'beacon-interest'
	…

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join.sql

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Implicit vs Explicit INNER

“INNER” is implied if you just write “JOIN”, so it’s not required, but can make the query easier to read. Either way is fine.

	# Good: Specifying INNER

	…
	select a.email
	, a.created_at as expressed_interest_at
	from helpscout.conversation a
	INNER JOIN
	helpscout.conversation_tag b
	ON a.id = b.conversation_id
	where b.tag = ‘beacon–interest’
	…

	# Good: Omitting INNER

	…
	SELECT HC.`email`, DATE_FORMAT(HC.`created_at`, '%Y-%m-%e %T UTC') AS expressed_interest_at
	FROM `helpscout.conversation` AS HC
	JOIN `helpscout.conversation_tag` AS HCT ON HC.`id`=HCT.`conversation_id` WHERE HCT.`tag`='beacon-interest'
	ORDER BY expressed_interest_at
	…

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inner.sql

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Filtering in the WHERE clause vs JOIN condition

The standard way to filter the conversations is to use a WHERE clause to filter the results to only include those that have a beacon-interest tag. However, because we’re using an INNER JOIN, it’s also possible to add it as a join condition and get the same result. In terms of performance, it doesn’t make a difference which approach you take.

I lean towards a WHERE clauses because I think it’s clearer, but including it in the JOIN condition is completely viable as well.

	# Good: Filtering in the WHERE clause

	…
	SELECT cnv.email,
	cnv.created_at AS expressed_interest_at
	FROM helpscout.conversation AS cnv
	JOIN helpscout.conversation_tag AS ctg
	ON cnv.id = ctg.conversation_id
	WHERE ctg.tag = 'beacon-interest'
	…

	# Good: Including it in the join condition

	…
	select conv.email, conv.created_at as expressed_interest_at
	from helpscout.conversation conv
	join helpscout.conversation_tag tag
	on conv.id = tag.conversation_id and tag.tag = 'beacon-interest'
	…

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join-vs-where.sql

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Converting Milliseconds

HubSpot stores the form submission timestamp in milliseconds. Queries that didn’t account that would not result in the correct result.

	# Good: Accounting for the timestamp being in millseconds

	…
	select email, (timestamp '1970-01-01 00:00:00 UTC' +
	numtodsinterval(property_beacon_interest/1000, 'SECOND'))
	at time zone 'utc') as created_at
	from hubspot.contact
	…

	# Bad: Not realizing millseconds need to be handled differently

	…
	SELECT
	EMAIL
	,FROM_UNIXTIME(PROPERTY_BEACON_INTEREST) AS EXPRESSED_INTEREST_AT
	FROM HUBSPOT.CONTACT
	WHERE
	PROPERTY_BEACON_INTEREST IS NOT NULL
	…

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milliseconds.sql

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UNION DISTINCT vs UNION ALL

There are two types of UNIONs: UNION DISTINCT and UNION ALL. The former – which is the default when you just write UNION – only returns the unique records in the combined results.

Both result in the correct answer here, but UNION ALL performs better because with UNION DISTINCT the database has to sort the results and remove the duplicate rows.

	# Good: UNION ALL

	SELECT
	C.email,
	C.created_at AS expressed_interest_at
	FROM helpscout.conversation C
	INNER JOIN helpscout.conversation_tag CT ON CT.conversation_id = C.id
	WHERE CT.tag = 'beacon-interest'
	UNION ALL
	SELECT
	email,
	DATEADD(S, CONVERT(INT,LEFT(property_beacon_interest, 10)), '1970-01-01') AS expressed_interest_at
	FROM hubspot.contact

	# Okay: UNION DISTINCT

	SELECT email, MIN(created_at) AS expressed_interest_at
	FROM
	(SELECT a.email, MIN(created_at) AS created_at
	FROM conversation a
	INNER JOIN conversation_tag b
	ON a.id=b.conversation_id
	WHERE b.tag="beacon-interest"
	GROUP BY id
	UNION
	SELECT email,DATETIME(time_created/1000,'unixepoch')\|\| ' UTC' AS created_at
	FROM hubspot.contact
	WHERE time_created IS NOT NULL)
	GROUP BY email ORDER BY expressed_interest_at

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union.sql

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Accounting for Multiple Interest

Many applicants didn’t take into account that people could have expressed interest by filling out the HubSpot form and contacting support. Neglecting to account for this could result in multiple rows for individual email addresses.

	# Good: Taking the earliest timestamp for each email

	SELECT email, MIN(created_at) AS expressed_interest_at
	FROM
	(SELECT c.email, MIN(created_at) AS created_at
	FROM helpscout.conversation c, helpscout.conversation_tag ct
	WHERE c.id =ct.conversation_id
	AND ct.tag="beacon-interest"
	GROUP BY id
	UNION
	SELECT email,DATETIME(time_created/1000,'unixepoch')\|\| ' UTC' AS created_at
	FROM hubspot.contact
	WHERE time_created IS NOT NULL)
	GROUP BY email ORDER BY expressed_interest_at;

	# Bad: Missing it

	select cont.email, cont.property_beacon_interest as expressed_interest_at
	from hubspot.contact cont
	where cont.property_beacon_interest is not null
	union
	select conv.email, conv.created_at as expressed_interest_at
	from helpscout.conversation conv
	join helpscout.conversation_tag tag
	on conv.id = tag.conversation_id and tag.tag = 'beacon-interest'
	order by expressed_interest_at

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multiple-interest.sql

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GROUP BY Column Name vs Number

I lean towards using column numbers, but either is fine, and using the column name can have benefits. When there are 5+ columns (which is not an issue with this question), lean towards using column numbers which will be a lot more sane than typing out all the names (hat-tip Ray Buhr for this tip).

	# Good: Grouping by column number

	…
	select
	email,
	min(expressed_interest_at) as expressed_interest_at
	from unioned
	group by 1
	order by 2
	…

	# Good: Grouping by column name

	…
	SELECT a.email, min(b.created_at) AS expressed_interest_at
	FROM hubspot.contact AS a
	JOIN helpscout.conversation AS b ON a.email = b.email
	GROUP BY a.email
	…

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grouping.sql

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Single vs Multiple Lines When Listing Multiple Columns

Another common style difference is whether people put multiple columns on the same line or not. Either is fine, but I lean towards one column per line because I think it’s easier to read.

	# Good: Multiple columns per line

	…
	SELECT email, created_at AS expressed_interest_at
	FROM helpscout.conversation c
	…

	# Good: One column per line

	…
	SELECT
	email,
	FROM_UNIXTIME(property_beacon_interest/1000) as expressed_interest_at
	FROM
	hubspot.contact
	…

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lines.sql

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Comma First vs Comma Last

While not that common in the responses, it’s perfectly valid when listing columns on multiple lines to put the commas before the column name. The benefit is that you don’t have to add a comma to the previous line when adding a new column which also simplifies query diffs that you might see in your version control tool.

	# Good: Comma last

	…
	SELECT
	email,
	FROM_UNIXTIME(property_beacon_interest/1000) as expressed_interest_at
	FROM
	hubspot.contact
	…

	# Good: Comma first
	…
	select
	email
	,dateadd(s, convert(int,left(property_beacon_interest,10)), '1970-01-01')
	from hubspot.contact
	…

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comma-first.sql

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Comma-first folks tended to have software development backgrounds.

PS: For anyone interested in SQL coding conventions, I highly recommend checking out dbt’s coding conventions which have influenced my preferences here.

All Responses

We were fortunate to receive over 100 applications, most of which included an answer to the SQL question. I suspect if the application didn’t include this question, we would have had twice the number of applcants, but the presence of this question led some underqualified folks not to apply.

You can check out all 89 responses on GitHub.

If you have any suggestions on how to improve my query or feedback on any of this analysis, please drop a comment below. Happy querying!

3 thoughts on “Analyzing 89 Responses to a SQL Screener Question for a Senior Data Analyst Position”

Jason Dexter says:

August 10, 2022 at 8:49 am

Hi Matt. I’m transitioning from Sr. Analyst to Analytics Engineer and am currently working to develop expertise in SQL to support the move. Your article was VERY helpful to me. My solution was close to yours so that was reassuring. Your commentary throughout the article/analysis helped me clean up a bit of my own understanding and habits/practices so thank you for that 🙌

Take care.
-Jason

- Mazur says:
  
  August 14, 2022 at 1:48 pm
  
  Glad it was helpful! You might also like my SQL style guide: https://github.com/mattm/sql-style-guide
  
  - Jason Dexter says:
    
    August 18, 2022 at 9:59 pm
    
    Yes! I stumbled onto that a while back and have it as one of my top go to SQL style guides – appreciate you, Matt 😎

Matt Mazur

Analyzing 89 Responses to a SQL Screener Question for a Senior Data Analyst Position

Analyzing the Differences

SQL Casing

Common Table Expressions (CTEs) vs Subqueries

Meaningful CTE Names

INNER JOIN vs LEFT JOIN

Implicit vs Explicit INNER

Filtering in the WHERE clause vs JOIN condition

Converting Milliseconds

UNION DISTINCT vs UNION ALL

Accounting for Multiple Interest

GROUP BY Column Name vs Number

Single vs Multiple Lines When Listing Multiple Columns

Comma First vs Comma Last

All Responses

3 thoughts on “Analyzing 89 Responses to a SQL Screener Question for a Senior Data Analyst Position”

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Analyzing the Differences

SQL Casing

Common Table Expressions (CTEs) vs Subqueries

Meaningful CTE Names

INNER JOIN vs LEFT JOIN

Implicit vs Explicit INNER

Filtering in the WHERE clause vs JOIN condition

Converting Milliseconds

UNION DISTINCT vs UNION ALL

Accounting for Multiple Interest

GROUP BY Column Name vs Number

Single vs Multiple Lines When Listing Multiple Columns

Comma First vs Comma Last

All Responses

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3 thoughts on “Analyzing 89 Responses to a SQL Screener Question for a Senior Data Analyst Position”

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