04.11.26

How to Write a Job Spec That Attracts Genuine Databricks, Snowflake, and LLM Specialists

A job spec is usually the first filter in a hiring process, but most hiring managers treat it as a formality rather than a tool. For Databricks, Snowflake, or LLM engineering roles, that's the mistake that creates the problem downstream: specs that list tools instead of describing real work attract CVs that mirror the tool list back, with little underneath it. By the time a recruiter or hiring panel is screening candidates who all claim "Databricks experience" but can't explain what they actually built, the damage is done. Fixing this starts with how the spec itself is written — before a single CV lands in your inbox.

This guide walks hiring managers and HR partners through structuring a job spec so it surfaces candidates who've genuinely done the work, not just named it.

Prerequisites

Before you start drafting, gather the following:

• A clear picture of the actual project or problem the hire will work on — not just the job title or team structure

• Input from at least one technical stakeholder (an engineer, architect, or technical lead) who can sanity-check the detail level in the spec

• Awareness of which specific tools, platforms, or frameworks matter for this role, and which are "nice to have" versus genuinely load-bearing

• A willingness to describe constraints (scale, regulatory context, migration stage, team size) rather than just capabilities

Step 1: Start From the Project, Not the Tool List

The most common failure mode in technical job specs is leading with a list of tools — "Databricks, Snowflake, Python, Spark, Airflow" — and letting candidates self-select based on keyword recognition. Instead, start by describing the actual project the person will walk into.

Look at how real technical screening conversations expose the difference. In one case, a hiring need was framed not as "Databricks engineer required" but as a data governance consultant needed to "come in and write the policies for them, assess the governance, work with the people... and essentially roll out a new data governance platform for them through their migration and transformation projects". That framing gave the candidate something concrete to respond to — and the candidate's answer ("I pride myself as a data governance and data management professional... every transformation that they are making, there are standards that need to be followed") immediately showed depth that a tool-list spec would never have surfaced.

Action: Write one or two sentences describing the migration, build, or transformation programme the hire supports — what's moving from where to where, and why — before you mention any specific tool.

Step 2: Name the Specific Platform Context, Not Just the Product Name

"Databricks experience" means almost nothing on its own — Databricks, Snowflake, and Microsoft Fabric are frequently used interchangeably by candidates and recruiters alike, since they "all sort of do the same thing... just a bit different with each one". A spec that simply says "Databricks required" invites candidates who've touched the product once to list it alongside those who've built production pipelines on it.

Instead, specify the layer or component that matters for your project:

• Unity Catalog, if governance or access control is the focus — one experienced data governance professional distinguished themselves precisely by explaining that "Unity Catalog is the governance layer within Databricks" and describing how they set and enforce standards across conceptual, logical, and physical data models

• PySpark and notebook-based transformation work, if the role is hands-on pipeline building — a strong technical conversation drew out detail on "PySpark" and "the notebook sort of side of the transformation" specifically, rather than stopping at "Databricks" as a keyword

• Delta Lake, bronze/silver/gold layer design, and incremental processing, if data quality and reconciliation matter — a senior data engineer with banking experience described this layered approach in detail, including "data quality checks and validations" and "source-to-target reconciliation before the data goes downstream"

Action: Replace generic tool names in the spec with the specific sub-component, layer, or workflow the candidate needs to have actually built.

Step 3: Describe the Migration or Transformation Stage

Candidates with real experience can speak fluently about where a transformation programme sits — legacy system, in-flight migration, or post-migration optimisation — because they've lived through that arc. One candidate described a live project in detail: "we're just... probably near the end of a migration project, which is just moving away from our legacy systems... and kind of just unifying everything in Databricks. And then changing our consumption layer... from Power BI into ThoughtSpot". That level of specificity is what separates someone who has delivered a migration from someone who has read about one.

A job spec that simply says "experience with data migration" invites both types of candidate to apply. A spec that says "you'll support the transition from a legacy SQL Server estate to a unified Azure Databricks lakehouse, including consumption-layer changes" invites only the former to recognise themselves in the brief.

Action: State explicitly which stage of the data or AI transformation lifecycle this role sits in — discovery, build, migration, or post-migration stabilisation — so candidates can self-assess against lived experience, not just tool familiarity.

Step 4: Specify the Sector Context When It's Load-Bearing

For regulated industries — financial services, fintech, asset management — sector experience often matters as much as tool experience, because the two are intertwined. A hiring brief for a data governance consultant in fintech asset management specifically flagged that the end client was "quite big on the whole data risk regulation kind of side of things", and a strong candidate's answer showed exactly why that mattered: governance work there means "setting the standard, embedding the standard, and assuring that the standard is being followed by the engineers" in a way that's "defensible to regulators".

Similarly, a senior data engineer candidate with HSBC and HDFC banking background spoke directly to why financial-sector experience changes the work: "because it's a financial background, data accuracy is very, very important... so I always make sure we have proper validation and source-to-target reconciliation".

Action: If the role sits inside a regulated or compliance-heavy sector, say so in the spec, and describe what that means practically (audit trails, regulatory reporting, data risk) rather than assuming sector fit is implied by the job title.

Step 5: Ask for Evidence of Judgement, Not Just Exposure

Tool lists invite candidates to claim exposure. What you actually want evidence of is judgement — the ability to make design decisions, not just operate a platform. A candidate asked about DevOps and CI/CD experience was asked directly: "Have you led the end-to-end pipeline creation before?" — a question that tests for ownership, not familiarity.

Build this into the spec itself by phrasing requirements around decisions the person will need to make, for example:

• "You'll be responsible for designing the bronze/silver/gold layer structure for a new lakehouse build" rather than "Experience with Databricks required"

• "You'll lead the migration of the consumption layer from Power BI to a new BI tool, including stakeholder sign-off" rather than "BI tool migration experience preferred"

• "You'll set and enforce data governance standards across engineering teams" rather than "Data governance experience"

Action: For every requirement in the spec, ask: does this describe exposure to a tool, or ownership of a decision? Rewrite exposure-only requirements into ownership-based ones.

Step 6: Separate "Must Have Built This" From "Must Be Able to Learn This"

Not every tool mentioned in a spec needs deep prior experience — some genuinely are learnable on the job. But specs rarely distinguish between the two, which means candidates can't tell which requirements are genuinely non-negotiable. A candidate who is "equally experienced on these products like Fabric, Databricks, Snowflake" demonstrates that strong engineers often move fluently between platforms — so an overly rigid "must have 5 years on Snowflake specifically" requirement may exclude excellent candidates unnecessarily, while a vague "familiarity with cloud data platforms" requirement fails to filter at all.

Action: Mark each technical requirement in the spec as either "demonstrated delivery required" or "working knowledge sufficient," and write the spec language to match — this does more to filter genuine specialists than any amount of tool-naming.

Step 7: Have a Technical Stakeholder Review the Draft Before It Goes Live

A spec written entirely by HR or a hiring manager without technical fluency will, by default, lean on tool names because they're the easiest thing to list. Before publishing, have someone with genuine domain knowledge — an engineer, architect, or technical lead already on the team — read the draft and flag any requirement that's vague enough to be satisfied by keyword-matching alone.

This is the same principle that underpins how we qualify candidates once applications come in: our consultants are trained to understand "the how, why and where (impact), and individual drivers and motivators" behind a candidate's technical claims, not just the keywords on their CV. Getting a technical reviewer involved at the spec stage applies that same discipline a step earlier — before the search even starts.

Tips & Best Practices

• Write the spec the way you'd brief a recruiter verbally. The strongest screening conversations happen when the interviewer describes the actual project context first and lets the tool questions follow naturally from there — your spec should do the same.

• Avoid tool lists with no project context. A spec that reads "Databricks, Snowflake, Python, SQL required" gives candidates nothing to anchor a genuine answer to, and gives you no way to tell real experience from rehearsed keywords.

• Treat sector context as a requirement, not a preference, when it's genuinely load-bearing — particularly in regulated industries where domain knowledge changes how the technical work is actually done.

• Build in a layer of specificity even for "standard" platforms. Candidates who can speak to Unity Catalog, Delta Lake, or specific layer architecture (bronze/silver/gold) are showing you depth that "Databricks experience" alone cannot.

• Remember this is upstream work, not downstream filtering. A well-structured spec reduces the volume of keyword-matched CVs reaching screening in the first place, rather than relying entirely on later-stage technical interviews to catch the gap.

Troubleshooting

"We don't have anyone technical enough in-house to review the spec." This is exactly the gap a specialist recruitment partner is built to close — our consultants are trained daily on how to qualify technical claims against real project context, even where the hiring business doesn't have deep in-house technical fluency to draw on.

"Our last spec attracted dozens of CVs that all said the same thing." That's the signature of a tool-list spec rather than a project-context spec. Revisit Steps 1 through 3 above and rewrite the requirements section around the actual migration, build, or transformation stage before re-advertising.

"We're not sure which requirements are genuinely non-negotiable." Bring in a technical stakeholder or specialist recruitment partner at the drafting stage, not after the shortlist comes back. Getting this input early is advisory work, not just screening — the goal is to stop keyword-matched candidates from entering the funnel in the first place.

Summary

A job spec built around project context, platform-specific detail, transformation stage, and sector relevance will surface genuinely qualified Databricks, Snowflake, and LLM engineering candidates far more effectively than one built around a tool list. This isn't just a drafting exercise — it's the upstream fix to a problem that otherwise gets caught, expensively, at the screening stage. If your technical fluency in-house is limited, that's precisely where a specialist recruitment partner adds value before a search even begins — helping you write the brief that attracts the right people, not just the right keywords.