Adam Vande Ven recently shared something that caught my attention: "One of my favorite customers is hiring a Microsoft Fabric Data Engineer! Do you know Microsoft Fabric? Do you love T-SQL and/or dbt?"

That short message says a lot more than "job opening." It hints at where modern analytics teams are heading, what skills are becoming table stakes, and why timing and relationships matter in hiring. Adam also added that if someone is interested, they should "reach out" and he can "connect the dots before the job is officially posted." That is a subtle but important insight into how many strong data roles get filled.

In this post, I want to expand on what Adam is pointing to: the growing demand for Microsoft Fabric talent, the practical value of T-SQL and dbt, and how to approach opportunities that start as a conversation instead of a formal posting.

What Adam Vande Ven is really signaling

Adam is doing three things at once in a few lines:

He is validating demand: a real team is hiring now.
He is defining the profile: Microsoft Fabric plus T-SQL and/or dbt.
He is offering a shortcut: talk early, before the role becomes a public funnel.

Key insight: when a hiring need is shared publicly by a trusted connection, it is often because the team values signal (skills and fit) over volume (hundreds of applicants).

If you have been watching the data platform space, this makes sense. Microsoft Fabric has been moving from "new and interesting" to "we need someone who can run this well." When that shift happens, hiring starts to feel urgent.

Microsoft Fabric in plain terms (and why it changes the skill mix)

Microsoft Fabric bundles several analytics capabilities into one product experience: data integration, engineering, warehousing, real-time analytics, and BI, tied together with OneLake and a governance story that plugs into Microsoft ecosystems.

For a Data Engineer, the appeal is speed and coherence:

Fewer disconnected tools to stitch together
A clearer path from ingestion to modeling to reporting
A shared platform that business and technical teams can rally around

For employers, Fabric can reduce platform sprawl. For engineers, it raises expectations: you are not just writing pipelines, you are shaping a platform that multiple teams will depend on.

If you are coming from Azure Synapse, Power BI, or classic SQL Server environments, Fabric can feel like a natural next step. If you are coming from a dbt-first, cloud warehouse world, Fabric introduces a Microsoft-native way to do many of the same outcomes, but with different ergonomics and admin patterns.

Why T-SQL still matters (even when the stack modernizes)

Adam explicitly calls out T-SQL, and that is not nostalgia. T-SQL remains a core language for many Microsoft-aligned data teams because:

A lot of business logic still lives in SQL
Many organizations have deep SQL Server knowledge and assets
Query performance and cost control often come down to SQL fundamentals

Even in modern lakehouse patterns, strong SQL skills show up everywhere: validating ingestion, writing transformations, performance tuning, and supporting analysts.

If you want to position yourself for a Fabric Data Engineer role, treat T-SQL as a credibility anchor. Hiring managers often trust candidates who can reason about joins, window functions, incremental loads, and query plans under pressure.

Where dbt fits, and why "and/or" is telling

Adam also asked, "Do you love T-SQL and/or dbt?" That "and/or" matters.

It suggests the team is pragmatic about how they build transformations:

Some work may be done in SQL-native patterns
Some work may be modeled with dbt conventions (tests, docs, modular models)
They may be integrating dbt-style workflows, even if the runtime differs

dbt, beyond the tool, represents a philosophy: version-controlled analytics engineering, modular transformations, and disciplined environments. If you can speak that language (even if your day job is not literally dbt), you are signaling maturity in how you ship data products.

Key insight: many teams are not choosing between SQL and dbt. They are choosing engineers who can build reliable transformations with modern discipline.

What a strong Microsoft Fabric Data Engineer profile looks like

If you are wondering what to emphasize when you "reach out" (as Adam invited), here is a practical checklist.

Technical foundations

Solid SQL (T-SQL specifically helps in Microsoft-heavy environments)
Data modeling: star schemas, dimensional modeling, and pragmatic denormalization
Incremental processing patterns and late-arriving data handling
Observability basics: logging, alerting, and data quality checks

Fabric-adjacent capabilities

Lakehouse and warehouse concepts, and when to use each
Orchestration and ingestion design (batch and, if relevant, streaming)
Workspace and environment management, including security basics
Understanding how BI consumers use the outputs (Power BI literacy is a plus)

Delivery habits that hiring managers love

Source control and CI/CD mindset
Clear documentation that reduces tribal knowledge
Ability to translate business questions into data contracts

You do not need to claim expert-level mastery in everything. But you should show you can learn fast and ship responsibly.

The hidden advantage: "before the job is officially posted"

Adam offered to "connect the dots" before the role is posted. That is not favoritism. It is often efficiency.

Public job postings create a flood of applicants, many of whom are not a fit. Early conversations let the hiring team:

Validate the role scope and seniority with real candidates
Identify must-have versus nice-to-have skills
Move quickly on strong fits before calendars fill up

For candidates, the upside is huge: you can get context that a job description never includes, like what success looks like in the first 30-60-90 days, what is currently broken, and how the team measures impact.

If you ever get an invitation like Adam's, take it seriously and respond professionally.

A simple outreach message that works

Keep it short, specific, and evidence-based:

One sentence on relevant platform experience (Fabric, Synapse, Azure, SQL Server)
One sentence on transformation approach (SQL, dbt, testing, documentation)
One concrete example of impact (cost reduction, refresh reliability, delivery speed)
A request for a quick call

The goal is to make it easy for the connector to vouch for you.

Why this LinkedIn post works (and what to learn from it)

Even though Adam's post is a hiring note, it is also a great example of effective LinkedIn content.

It is specific (Microsoft Fabric Data Engineer)
It is conversational (questions that invite replies)
It creates urgency (before the job is officially posted)
It leverages trust ("one of my favorite customers")

If you are building your own presence, this is a solid content strategy: make your posts easy to respond to, grounded in real work, and useful to a defined audience. That is often how "viral posts" happen in professional niches, not through gimmicks.

If you are hiring: what Adam's approach gets right

From the hiring side, Adam shows a playbook worth copying:

Share a clear role title and a tight skill signal
Invite direct contact to reduce noise
Use the network as a pre-filter

In specialized markets like data engineering, warm introductions frequently beat cold applications.

Closing thought

Adam Vande Ven's quick note is a reminder that the data job market is not just about platforms, it is about timing and relationships. Microsoft Fabric is gaining momentum, and teams want engineers who can combine solid SQL execution with modern transformation practices like dbt. If you have that blend, the best opportunities may start with a message, not a job board.

This blog post expands on a viral LinkedIn post by Adam Vande Ven. View the original LinkedIn post →