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If you’ve Googled “Data Modeler job description,” chances are you’ve landed on a wall of buzzwords and bullet points.
Things like:
“Build conceptual, logical, and physical data models using ERwin or similar tools.”
Sure, that’s part of the job—but that’s not what attracts great candidates.
Here’s the problem: most job descriptions are written like compliance documents. They list tools, tasks, and requirements, but forget that the best candidates don’t apply just because they can do the job—they apply because they want to.
If your job post feels like a formality, you’re missing the opportunity to stand out. Especially in a role like Data Modeler, where the best candidates care deeply about clean systems, collaboration, and long-term data strategy—not just day-to-day SQL work.
Before we get into examples, if you haven’t already, check out our full guide on how to write a job post that attracts top talent , Link https://workscreen.io/how-to-write-a-job-post/. It walks through everything you need to know—from structure to tone to candidate psychology.
We’ll break down:
- What the role actually is (in plain English)
- Two great job description templates (one for experienced hires, one for entry-level)
- A breakdown of why these templates work
- A bad example (and why it fails)
- Copy-paste versions for quick use
- Plus tips on how to use AI responsibly when creating your job post
Let’s dive in.
Don’t let bad hires slow you down. WorkScreen helps you identify the right people—fast, easy, and stress-free.
What Does a Data Modeler Actually Do? - Their Roles
A Data Modeler is someone who designs the structure of how data is stored, organized, and accessed across your business.
Think of them as architects for your company’s data—they don’t just build the database, they make sure the foundation is clean, scalable, and easy for teams to work with. They take messy, scattered data and turn it into something structured, efficient, and usable.
A great data modeler helps you:
- Design logical and physical data models that reflect how your business actually operates
- Prevent data chaos by creating consistent naming conventions and schema standards
- Collaborate with engineers, analysts, and business teams to make sure the data is actually usable
- Future-proof your systems by building scalable, well-documented models
In short, a Data Modeler doesn’t just organize data—they make it easier for everyone in your company to make smarter, faster decisions.
Two Great Data Modeler Job Description Templates
✅ Job Post Template #1 – Job Description For Experienced Data Modeler
📌 Job Title: Senior Data Modeler — Scale Our Data Infrastructure
📍 Location: Hybrid | San Francisco HQ or Remote (PST overlap)
💼 Type: Full-Time (Permanent) | $110K–$140K salary + 0.3–0.5% equity
🕒 Hours: Flexible core hours 10 AM–3 PM PST
🎥 Message From Our Head of Data (2 min)
[Embed Loom or YouTube link]
Who We Are — MetricForge
MetricForge is a Series A SaaS startup helping 300+ e-commerce brands unify marketing, sales, and ops data into a single, decision-ready source of truth. Our real-time dashboards power $4.2 billion in annual GMV decisions for customers like AllBirds and Haus Labs.
Our Culture
- Craftsmanship first: We ship fast and insist on clean, well-documented code.
- Curiosity over credentials: We value thoughtful questions more than fancy résumés.
- Trust through transparency: Everyone sees the same weekly metrics—including cash.
- Win together: Quarterly “maker weeks” pair engineers, analysts, and product in small squads.
Why This Role Is a Great Fit
You’ll own the blueprints that keep our data warehouse performant as data volume triples each year. If you love turning data chaos into elegant models that unlock business insight—and influencing data strategy across teams—you’ll thrive here.
Perks & Benefits
- 100 % employer-paid medical, dental, vision
- 4-week paid sabbatical after 3 years
- $2,000 annual learning stipend
- 20 PTO days + 5 flex days + all US holidays
- Choose your own hardware (M1/M2 MBP or equivalent)
- Quarterly off-sites in Napa, Portland, or Denver
Key Responsibilities
- Design & maintain logical/physical models in dbt + Snowflake
- Optimize schema for cost & query performance (sub-second Looker dashboards)
- Document lineage, definitions, and governance in Confluence
- Partner with Data Engineering to evolve ELT pipelines
- Lead modeling for new product features (real-time cohorting, LTV analytics)
Requirements
- 3+ yrs data modeling or data engineering experience
- Advanced SQL and dimensional modeling (star / snowflake, 3NF)
- Experience with dbt, Snowflake (or Redshift/BigQuery), and Git
- Excellent cross-functional communication
Hiring Process
- WorkScreen skills-first assessment
- 30-min intro call with hiring manager
- Technical case exercise (async)
- Culture-add chat with VP Eng
- Offer & reference check
Apply here → [WorkScreen link]
✅ Job Post Template #2 – Job Description For Entry-Level / Willing-to-Train
📌 Job Title: Junior Data Modeler — Learn & Grow With Us
📍 Location: Remote (U.S.)
💼 Type: Full-Time | $65K–$80K salary
🕒 Hours: Monday–Friday | 9 AM–5 PM EST
🎥 Meet the Team (90 sec)
[Embed Loom or YouTube link]
Who We Are — DataNest
DataNest is a bootstrapped healthcare-data SaaS trusted by 600+ outpatient clinics to unify patient, billing, and scheduling data. By transforming siloed systems into one HIPAA-secure dashboard, we help clinics reclaim an average of 12 hours of admin time per week.
Our Culture
- People first: We offer “wellness Wednesdays” with no internal meetings.
- Learn by doing: Every junior hire gets a senior mentor & a $1K education budget.
- Bias for clarity: We document decisions asynchronously so remote teammates stay in sync.
- Give back: We donate 1 % of revenue to rural-health nonprofits.
Why This Role Is a Great Fit
You’ll receive hands-on coaching from senior data engineers while tackling real modeling problems that directly improve patient care workflows. If you’re naturally curious and eager to translate messy healthcare data into clean insights, you’ll make a tangible impact from day one.
Perks & Benefits
- 100 % remote with a $500 home-office stipend
- Health, dental, and vision insurance
- 15 PTO days + 10 paid holidays
- $1,000 annual education allowance
- Paid volunteer hours (8 per year)
- Quarterly virtual team retreats (escape-room, cooking classes, etc.)
Key Responsibilities
- Assist in designing and documenting data models (under mentor guidance)
- Clean CSV/EHR exports and load into Snowflake
- Maintain model dictionaries & lineage docs
- Join daily stand-ups and sprint reviews
- Participate in code reviews to learn best practices
Requirements
- Basic SQL or spreadsheet fluency
- Strong attention to detail & problem-solving mindset
- Comfortable asking questions and giving status updates
- Bonus: coursework or bootcamp in data analytics
Hiring Process
- WorkScreen skills assessment (no résumé needed)
- 20-min intro chat
- Paid 10-hour mini-project (flexible schedule)
- Team-fit interview
- Offer with clear 90-day growth plan
Ready to start? Apply here → [WorkScreen link]
WorkScreen simplifies the hiring process, helping you quickly identify top talent while eliminating low-quality applications. By saving you countless hours and reducing the risk of bad hires, it empowers you to build a team that delivers results
Why These Data Modeler Job Posts Actually Work
Let’s break down what makes these two job descriptions effective—and why they stand out from the generic templates you’ll find online.
✅ 1. The Job Titles Are Clear, Specific, and Purposeful
Instead of the vague “Data Modeler” or “Database Specialist,” these titles include:
- Seniority level (Senior / Junior)
- Mission or outcome (“Scale Our Data Infrastructure” / “Learn & Grow With Us”)
- Clarity on format or location (Hybrid, Remote, Salary Range)
A good job title isn’t just informative—it attracts the right person and repels the wrong one.
✅ 2. Each Post Starts With a Human Touch
Both descriptions include a Loom video from the hiring manager or team. This adds a face to the name, shows that someone cares, and makes the process feel personal—not robotic.
Great candidates want to work with real people, not just job listings.
✅ 3. Company Descriptions Are Mission-Driven and Specific
Instead of the usual “we’re a growing SaaS company,” these “About Us” sections:
- Name the industry and customer type
- Mention traction (e.g., # of users or revenue)
- Connect the role to the broader mission
This builds trust and gives context. The reader can picture who they’re helping and how they’ll make an impact.
✅ 4. Culture Isn’t Just Claimed—It’s Shown
Rather than generic statements like “we’re collaborative,” the posts describe actual behaviors:
- “Quarterly ‘maker weeks’ across teams”
- “Wellness Wednesdays with no meetings”
- “Everyone sees the same weekly metrics”
That’s how you show culture instead of just saying you have one.
✅ 5. There’s a Dedicated Section for Perks & Benefits
Benefits aren’t buried. They’re clearly listed with specifics:
- Healthcare, PTO, learning stipends
- Flex hours and home office stipends
- Off-sites, sabbaticals, paid volunteering
Transparency here builds trust and signals maturity. The best candidates want to know the full offer—up front.
✅ 6. The “Why This Role Is a Great Fit” Section Sells the Opportunity
This is your chance to pitch the job to the candidate. These posts highlight:
- What success in the role looks like
- Why the role matters to the company
- What makes this job meaningful and energizing
Most job posts skip this—and lose great talent because of it.
✅ 7. The Responsibilities Are Written With Context
Rather than vague bullet points, each duty is tied to an outcome or explained in plain English. For example:
- “Design & maintain logical/physical models in dbt + Snowflake”
- “Assist in designing and documenting data models (under mentor guidance)”
This helps candidates visualize what they’ll actually be doing—not just check boxes.
✅ 8. The Hiring Process Is Transparent and Respectful
Each job post outlines every step—from application to offer—with estimated timelines and expectations. It also reassures applicants they’ll receive a response.
This small detail builds psychological safety—and makes your company stand out in a world where most candidates are ghosted.
✅ 9. WorkScreen is Seamlessly Integrated
Rather than forcing candidates through a long résumé review, each post invites them to show what they can do. By using WorkScreen:
- You assess skills, not just experience
- Candidates feel they’re being evaluated fairly
- You avoid wasted interviews with unqualified or AI-generated applications
It also filters out low-effort applicants right from the start.
Example of A Bad Data Modeler Job Post (And Why It Fails)
Let’s look at a typical job post you might find online—and why it’s quietly repelling great candidates.
❌ Bad Job Post Example: Data Modeler
Job Title: Data Modeler
Company: DataX Solutions
Location: New York, NY
Type: Full-Time
Salary: Not Disclosed
Job Summary
DataX Solutions is seeking a Data Modeler responsible for designing and implementing data models to support enterprise reporting and analytics. The ideal candidate will work cross-functionally to develop data structures that optimize performance and support business requirements.
Responsibilities
- Develop and maintain conceptual, logical, and physical data models
- Create data dictionaries and ER diagrams
- Analyze business needs and translate into data requirements
- Ensure data integrity and compliance with standards
- Collaborate with IT teams on data-related initiatives
Requirements
- Bachelor’s degree in Computer Science or related field
- 3+ years of experience in data modeling
- Strong SQL and data warehousing knowledge
- Experience with ERwin or other modeling tools
- Excellent analytical and communication skills
How to Apply
Send your CV and cover letter to careers@dataxsolutions.com. Only shortlisted candidates will be contacted.
❌ Why This Job Post Falls Flat
1. The Job Title Is Too Generic
Just “Data Modeler” tells you what the role is, but not who it’s for, what kind of work they’ll do, or why it matters. There’s no hint of purpose, seniority, or specialization.
2. The Introduction Is Cold and Corporate
It’s a dry, jargon-heavy summary that doesn’t say anything about the company’s mission, the product, or why this role even exists. A top candidate won’t connect with this—and likely won’t apply.
3. There’s No Culture or Human Connection
Nothing about the team, how the company works, or what the values are. No “who you’ll work with,” no insights into the work style or environment.
4. Salary and Perks Are Missing
No salary range, no benefits, no PTO info. Today’s candidates want transparency. Leaving this out creates friction—and signals an outdated approach to hiring.
5. Responsibilities Are Vague and Generic
The bullet points could apply to any data modeling job. There’s no context or outcome. For example, “Develop data models” is technically accurate… but it doesn’t tell the applicant why or how they’ll matter.
6. Application Process Is Cold and Dismissive
“Only shortlisted candidates will be contacted” immediately puts the candidate in a passive, powerless position. There’s no timeline, no sense of respect for their time, and no commitment to communication.
7. No Personality in the CTA
There’s no energy, no encouragement, and no reason to apply beyond necessity. This makes the post feel like a chore instead of an opportunity.
Bottom Line:
This job post might check the basic boxes, but it doesn’t connect. It doesn’t sell the opportunity. And that means great candidates will scroll past it—and never come back.
Bonus Tips to Make Your Data Modeler Job Post Stand Out
You’ve already seen what a great job post looks like. But if you want to go one step further—and truly attract high-quality candidates while building trust—here are some small, strategic additions that make a big difference.
✅ Tip 1: Add a Security & Privacy Disclaimer
Scammers sometimes target job seekers. Adding a small note shows that you care and builds immediate trust with applicants.
🔒 IMPORTANT: We take your privacy seriously. We will never ask for sensitive financial information, bank details, or any form of payment during the hiring process.
This helps legitimate your post and reduces candidate hesitation—especially for remote roles.
✅ Tip 2: Mention Leave or Flex Days
Time off matters. If your company offers paid time off, flex days, or mental health days, include that early. It shows you value work-life balance.
“Enjoy 20 PTO days + 5 flex days per year to recharge and avoid burnout—because doing great work starts with taking care of yourself.”
✅ Tip 3: Highlight Learning, Mentorship, or Growth Paths
Data roles attract analytical minds who want to grow. Call out your commitment to professional development:
“We invest in your growth: Every team member receives a $2,000 annual learning stipend, 1-on-1 mentorship, and a clear path to grow into senior roles.”
This is especially useful for junior/entry-level posts, but even senior candidates appreciate knowing they won’t stagnate.
✅ Tip 4: Include a Loom or Video Message From the Team
You’ve seen it already in the good examples—but it’s worth repeating. A 60–90 second Loom from the hiring manager or founder saying:
- Why this role matters
- What success looks like
- What kind of teammate they’re hoping for
…makes your job post feel alive and personal.
Pro Tip: Even a selfie-style video works. The authenticity matters more than production quality.
Here is an example that we used in our master guide on how to write a great job post description , you can check it out here https://www.loom.com/share/ba401b65b7f943b68a91fc6b04a62ad4
✅ Tip 5: Clarify Timeline and Application Expectations
Set the tone for a respectful hiring process by letting applicants know what comes next.
“We respond to all applicants within 7 business days. If you’re shortlisted, we’ll invite you to a quick intro call and skills evaluation via WorkScreen. Either way, you’ll hear from us.”
This one sentence eliminates the anxiety and confusion most candidates feel after hitting “apply.”
✅ Tip 6: Make the Role’s Impact Clear
Top candidates want to know they’re not just filling tickets—they want to build something meaningful. So tell them:
“This role will shape the foundation of our data systems as we scale to 10x more users in the next 12 months. You’ll play a key role in how decisions get made across engineering, growth, and product.”
This helps candidates feel the weight—and opportunity—of the work.
Should You Use AI to Write a Job Description?
Let’s be honest—generative AI tools like ChatGPT, Jasper, and even ATS platforms like Workable or Manatal are making it really easy to auto-generate job posts with one click.
And at first glance, it seems like a time-saver.
But here’s the hard truth:
🚨 If you use AI the wrong way, you’ll end up with a generic, soulless job post that repels the very people you want to hire.
❌ The Wrong Way to Use AI
You drop a lazy prompt like:
“Write me a job description for a Data Modeler.”
…and get back five bullet points, some buzzwords, and a lifeless job summary that could’ve been written in 2006.
The result?
- You attract the wrong candidates—those who mass-apply without reading
- You blend in with every other company using the same tools
- You miss the chance to show your culture, mission, and tone of voice
✅ The Smarter Way to Use AI
AI is powerful when used as a collaborator—not a replacement.
Before you prompt it, come prepared with the raw ingredients that make your company and role unique.
Here’s how:
🧠 The Prompt That Works
“Help me write a job post for our company, DataNest.
We’re hiring a Junior Data Modeler to help unify and structure healthcare operations data from hundreds of clinics.
Our company culture is collaborative, flexible, and mission-driven.
We’re looking for someone who’s curious, detail-oriented, and eager to learn.
We offer full remote work, $65K–$80K salary, health benefits, a $1,000 learning stipend, and 15 PTO days.
Our hiring process includes a WorkScreen skills test, intro call, and a paid mini-project.
Here are some notes I’ve written to get you started: [Paste your rough draft here].”
This gives AI a direction—and lets it shape, polish, and organize your message while keeping your authentic tone and values intact.
You can even reference real job posts you like and ask AI to mirror that structure.
✏️ Pro Tip:
Use AI to help:
- Reword confusing sections
- Add clarity and tone
- Tighten up long paragraphs
- Suggest creative phrasing
- Fix grammar
But don’t let it write your brand voice from scratch. That part still needs you.
If your hiring process is stressful, slow, or filled with second-guessing—WorkScreen fixes that. Workscreen helps you quickly identify top talent fast, eliminate low-quality applicants, and make better hires without the headaches.
Need a Quick Copy-Paste Job Description? Start Here
✅ Option 1: Conversational Job Description Template (Culture-First Style)
📌 Job Title: Junior Data Modeler — Build, Learn, and Grow With Us
📍 Location: [Insert Location or “Remote”]
💼 Type: [Insert Job Type] | [Insert Salary Range]
🕒 Hours: Monday–Friday | [Insert Working Hours]
🎥 Meet the Hiring Manager (60–90 sec)
[Insert Loom or YouTube Link]
Who We Are
At [Company Name], we help [briefly describe what your company does and who it serves]. Our mission is simple: [insert short mission]. We work with [mention any industries, customers, or product categories], and our platform makes it easier for teams to [explain the core outcome or benefit].
We’re a collaborative, mission-driven team that values curiosity, trust, and clarity. If you care about building meaningful systems and want to grow in a fast-moving environment, you’ll feel right at home here.
Why This Role Is a Great Fit
- You’re analytical but love keeping things simple
- You’re early in your career and want to learn fast
- You’re organized, reliable, and enjoy solving puzzles
- You want mentorship and real ownership—not busy work
- You’re excited by the idea of cleaning messy data and turning it into real business impact
Perks & Benefits
- 💻 Remote-first with flexible hours
- 🏥 Medical, dental, and vision insurance
- 📚 $1,000 annual learning stipend
- 🌴 [Insert number] PTO days + paid holidays
- 🧘 No internal meetings one day a week
- 💡 Mentorship and clear promotion paths
What You’ll Be Doing
- Assist in designing and documenting internal data models
- Clean and structure data from spreadsheets, APIs, or exports
- Maintain data dictionaries and track model changes
- Join daily team meetings and collaborate with mentors
- Shadow senior teammates and learn best practices
What We’re Looking For
- Familiarity with SQL or spreadsheets
- Attention to detail and strong organization skills
- Good communication and eagerness to learn
- Bonus: hands-on experience through coursework or bootcamps
How to Apply
We use WorkScreen to evaluate candidates fairly based on real skills, not just résumés. Click below to take a quick, role-specific evaluation.
👉 [Insert WorkScreen Application Link]
🧱 Option 2: Structured Job Description Template (Job Brief + Responsibilities + Requirements)
📌 Job Title: Data Modeler
📍 Location: [Insert Location or “Remote”]
💼 Type: [Insert Job Type] | [Insert Salary Range]
🎥 Meet the Team (Short Video)
[Insert Loom or YouTube Link]
Who We Are
[Company Name] helps [insert industry or audience] make sense of complex data. Our tools turn disconnected systems into clean, centralized insights that drive real decisions.
We believe in building scalable systems, documenting as we go, and designing with the long-term in mind. Now we’re looking for a Data Modeler to help us take our data infrastructure to the next level.
Our Culture
- We move fast but document everything
- We care more about clarity than perfection
- We value team wins over solo glory
- We communicate openly and give feedback often
- We respect your time and your life outside of work
Perks & Benefits
- 🏥 Health, dental, and vision insurance
- 💻 Work-from-home flexibility
- 📚 $1,000+ learning and development budget
- 🌴 [Insert number] PTO days per year
- 💬 Async-first communication culture
- 🌱 Growth plans and internal mentorship
Job Brief
As a Data Modeler, you’ll be responsible for designing clean, logical data models that serve both analytics and engineering needs. You’ll collaborate with cross-functional teams to build scalable systems that make it easy to extract insights and build new features.
Key Responsibilities
- Create and maintain data models (logical + physical)
- Collaborate with data engineers on warehouse structure
- Define naming conventions, data dictionaries, and documentation
- Review data flows and propose improvements
- Support new product and reporting initiatives with model design
Requirements
- 2+ years experience in data modeling or engineering
- Proficiency in SQL and modeling tools (dbt, ERD, etc.)
- Strong documentation habits and attention to detail
- Clear communication with technical and non-technical teams
- Bonus: experience with cloud warehouses (Snowflake, Redshift, BigQuery)
How to Apply
We use WorkScreen to make the hiring process fair and skills-based.
Start your application here: 👉 [Insert Link]
Let WorkScreen Handle the Next Step of Hiring
Writing a great job description is only half the battle. The real challenge? Figuring out who’s actually qualified once the applications start rolling in.
That’s where WorkScreen comes in.
Instead of spending hours reading through polished résumés and copy-pasted cover letters, you can:
✅ Quickly spot your most promising candidates
WorkScreen automatically evaluates, scores, and ranks applicants based on real-world skills—not just what they claim on paper. You get a performance-based leaderboard that makes it easy to see who’s worth your time.
✅ Assess candidates with one-click skill tests
Whether you’re hiring a Data Modeler, Sales Manager, or Customer Support Rep, you can use simple, role-specific assessments to understand how each applicant thinks, works, and solves problems.
No guesswork. No surprises later.
✅ Filter out low-effort or AI-generated applications
Let’s face it—anyone can use ChatGPT to write a nice cover letter. But with WorkScreen, you eliminate ghost applicants, one-click appliers, and people gaming the system.
That means you spend less time reviewing bad fits—and more time interviewing serious candidates who are truly excited to work with you.
Ready to hire smarter, faster, and more confidently? 👉 Sign up at WorkScreen.io and create a job post in minutes. Get a shareable link, launch your application, and let WorkScreen do the heavy lifting.
FAQ
A Data Modeler designs the structure and flow of how data is stored, organized, and accessed. They create schemas, optimize database architecture, and ensure systems can scale efficiently.
A Data Analyst, on the other hand, focuses on interpreting that data. Analysts run queries, create reports, and extract insights to support business decisions—but they typically rely on the models built by data engineers or modelers.
In short: a Data Modeler builds the foundation, while a Data Analyst tells the story on top of it.
Here are some key skills to look for in a high-quality data modeler:
- Advanced SQL – writing optimized, scalable queries
- Dimensional modeling – including star/snowflake schema design
- Data warehousing experience – tools like Snowflake, BigQuery, or Redshift
- Modeling tools – such as dbt, ERwin, Lucidchart, or draw.io
- Documentation discipline – naming conventions, lineage tracking, and dictionaries
- Collaborative mindset – works well with engineers, analysts, and product teams
- Systems thinking – ability to see how data flows connect across departments
Bonus traits: curiosity, precision, and the ability to explain technical decisions clearly to non-technical teams.
As of 2025, the average salary for a Data Modeler in the U.S. ranges between $95,000 and $130,000, depending on experience, location, and industry.
- Entry-Level: $65K–$80K
- Mid-Level: $90K–$110K
- Senior-Level: $120K–$150K+
In tech-forward cities or remote-first startups, salaries often include equity or bonuses. Transparent compensation helps attract better candidates—so always include a range in your job post.
