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If you’ve Googled “Data Quality Analyst job description,” chances are you’ve run into the same problem:
A sea of bland, copy-paste templates filled with jargon, buzzwords, and lifeless bullet points.
They might list tasks like “maintain data integrity” or “analyze discrepancies”—but they completely miss the bigger picture:
What actually makes a great Data Quality Analyst?
Why does this role matter to your business?
And how do you write a job post that attracts someone who genuinely cares about data accuracy and impact—not just someone looking for any analyst role?
That’s what this guide is here to solve.
We’re not just giving you a template—we’re showing you how to write a human, effective job post that attracts high-quality candidates who take data seriously.
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.
Now let’s talk about what this role actually is.
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
What A Data Quality Analyst Actually Does - Their Roles
A Data Quality Analyst is the person who ensures your business decisions are based on clean, accurate, and trustworthy data.
They don’t just fix typos in spreadsheets—they build the guardrails that keep your data reliable. They monitor data pipelines, find inconsistencies, audit systems, and make sure everyone—from finance to marketing—is working from a single source of truth.
Think of them as the quality control team for your data:
They catch the small errors before they become costly mistakes.
But it’s not just about tools and reports. The best data quality analysts are curious, detail-obsessed, and proactive. They ask questions like:
“Why does this number look off?”
“Where is this data coming from?”
“Is this field being entered the same way across teams?”
In short, they help your team move fast without breaking things—and that makes them one of the most quietly essential hires you can make.
Two Great Data Quality Analyst Job Description Templates
✅ Option 1 — Job Description For Experienced Data Quality Analyst (Culture-First Style)
📌 Job Title: Data Quality Analyst – Keep NovaMetrics’ Data Spotless & Decision-Ready
📍 Location: Hybrid • 2 days/week in our Kilimani, Nairobi office
💼 Type: Full-Time | Data & Analytics Team
💰 Salary: KES 140,000 – 180,000 / month (DOE)
🎥 Quick Video Introduction
Watch a 90-second welcome from Grace Kimani, our Head of Data, and see why data quality is the bedrock of every project we ship. — [Loom link]
🏢 Who We Are (NovaMetrics)
Founded in 2019, NovaMetrics is a 40-person analytics consultancy that helps East-African fintech and retail brands turn messy data into clear, business-shaping insight. Our clients include Equity Bank, Twiga Foods, and several fast-scaling SaaS startups. We obsess over accuracy, speed, and practical storytelling—because decisions only matter when they’re based on truth.
💡 What You’ll Be Doing
- Design & automate data-validation tests (Great Expectations / dbt)
- Trace anomalies back to source systems and fix root causes
- Maintain living data dictionaries & business-rule docs
- Collaborate with data engineers to harden pipelines in Snowflake
- Partner with analysts to certify dashboards before launch
- Propose process tweaks that keep bad data out in the first place
🧠 What We’re Looking For
- 2–3 yrs in data-quality / data-ops / BI role
- Confident SQL problem-solver (complex joins, CTEs, window functions)
- Hands-on with a modern data stack (Snowflake, BigQuery, dbt, or similar)
- Detail-obsessed; can explain anomalies to non-data teammates
- Bonus: experience inside a consultancy or high-growth startup
⭐ Why This Role Is a Great Fit
We’re doubling the size of our analytics practice, and you’ll be the first full-time hire dedicated to data quality. You’ll own the guardrails that keep our insights credible—directly impacting millions of shillings in client decisions every quarter. If you like autonomy, visible impact, and a team that sweats the small stuff, you’ll thrive here.
🎁 Perks & Benefits
- Fully covered medical, dental, and vision insurance
- 24 flex days off per year + Kenya public holidays
- Monthly remote-work stipend (KES 8,000)
- Annual professional-development budget (KES 60k)
- New-hire laptop + any software you need
- Profit-share bonus when we hit quarterly team targets
📥 How to Apply
We hire through WorkScreen.io to keep the process fair and skills-first. Start with a short data-quality challenge; if it clicks, we’ll invite you to a 30-minute chat with Grace. We respond to every applicant.
👉 [WorkScreen link]
✅ Option 2 — Job Description For Entry-Level / Willing-to-Train Data Quality Analyst
📌 Job Title: Junior Data Quality Analyst – Learn & Grow at Lumera Analytics
📍 Location: Remote-Friendly (Kenya or ±3 hrs EAT)
💼 Type: Full-Time | Data Operations
💰 Salary: KES 80,000 – 110,000 / month to start (6-month review)
🎥 Quick Video Introduction
Hear from Alex Mburu, our Data Lead, on how he’ll mentor you through your first SQL query to your first automated data test. — [Loom link]
🏢 Who We Are (Lumera Analytics)
Lumera Analytics is a VC-backed SaaS startup (founded 2022, team 25) that provides an end-to-end customer-analytics platform for African e-commerce brands. Our mission: help merchants make smarter growth decisions without hiring a data army. Clean data is our product’s heartbeat—so your role is mission-critical.
💡 What You’ll Be Doing (and Learning)
- Clean duplicate & missing records in BigQuery tables
- Document data-entry guidelines for client success teams
- Shadow senior analysts during dataset audits
- Learn SQL basics and eventually write your own validation queries
- Surface anomalies to engineering for quick fixes
- Keep asking “Why does this number look wrong?” until it’s right
🧠 What We’re Looking For
- Eagle-eye attention to detail; you notice when things don’t add up
- Comfortable with Google Sheets or Excel basics
- Hunger to learn SQL and modern data tools (training provided)
- Strong written communication—can explain findings clearly
- Bonus: any experience in research, QA, or operations
⭐ Why This Role Is a Great Fit
You’ll get hands-on mentorship, real ownership of bite-sized projects, and a front-row seat to a rapidly scaling SaaS company. Within six months, you’ll progress from spreadsheet checks to writing automated tests that protect live client dashboards—building a career path in data you can be proud of.
🎁 Perks & Benefits
- Comprehensive health cover + wellness stipend (KES 3,000 / month)
- 20 paid leave days + 5 company recharge days
- Home-office equipment grant (one-time KES 50k)
- Paid certification courses (e.g., Google Data Analytics)
- Remote meet-ups in Nairobi every quarter (travel covered)
- Profit-linked bonus pool after year 1
📥 How to Apply
All applications run through WorkScreen.io. Complete a short logic-and-detail challenge; top scorers move straight to a friendly interview with Alex. We reply to everyone—no résumé black holes.
👉 [WorkScreen link]
Don’t let bad hires slow you down. WorkScreen helps you identify the right people—fast, easy, and stress-free.
Why These Data Quality Analyst Job Posts Work (Breakdown)
Both job descriptions you just read may feel different from what you’re used to—and that’s the point. Let’s break down why they work and what makes them stand out in today’s hiring market:
✅ 1. The Job Titles Are Clear, Specific, and Purposeful
Instead of just saying “Data Quality Analyst,” each title adds something that makes it instantly more relevant:
- “Keep NovaMetrics’ Data Spotless & Decision-Ready” tells the candidate why the role matters.
- “Learn & Grow at Lumera Analytics” signals that the second role is beginner-friendly and growth-oriented.
That clarity attracts the right kind of person before they even read the body.
✅ 2. The Video Adds a Human Connection
A short Loom video from the hiring manager (Grace or Alex) gives the post personality and makes it feel real—not robotic.
It helps candidates visualize the people they’d be working with and sets the tone for a transparent, human hiring process.
✅ 3. The “About Us” Section Feels Real (Not Copy-Paste)
Instead of vague corporate jargon, each “Who We Are” section shares:
- The company’s mission
- The size and stage of the team
- Specific industries or clients
- The kind of work environment candidates can expect
This helps candidates decide if they see themselves thriving there.
✅ 4. Responsibilities Show Impact—Not Just Tasks
Instead of listing out boring tasks like “run quality checks,” the posts describe why each task matters:
- “Your work will power smarter decisions across product, ops, and finance.”
- “Clean data is our product’s heartbeat—so your role is mission-critical.”
This connects the candidate’s day-to-day to the company’s success.
✅ 5. The Requirements Are Honest and Inclusive
- The experienced role has clear technical expectations (SQL, Snowflake, Great Expectations).
- The entry-level post emphasizes potential and learning ability—not years of experience.
By clarifying what’s truly required vs. trainable, you attract qualified candidates without scaring off capable ones.
✅ 6. Perks and Benefits Are Transparent
Each post includes a dedicated Perks & Benefits section with real, specific offerings—not vague phrases like “competitive salary” or “great culture.”
This builds trust and shows that you value the candidate’s time and well-being.
✅ 7. The “Why This Role Is a Great Fit” Section Pitches the Opportunity
This is where most job posts fall flat—they don’t sell the job.
But in these examples, this section makes a strong case for why someone should care:
- What they’ll learn
- The level of ownership they’ll have
- What kind of impact they’ll make
- How they’ll grow
✅ 8. The Application Process Is Respectful and Modern
Each post:
- Sets expectations (WorkScreen test → interview)
- Assures every applicant gets a reply
- Links to WorkScreen to screen fairly and efficiently
This tells candidates: “We value your time, and we’re serious about hiring right.”
Example of a Bad Data Quality Analyst Job Post (And Why It Fails)
Let’s look at a version of the job post that looks like what you’ll find on most job boards. Then we’ll break down why it’s ineffective—and what it’s costing you in candidate quality.
❌ Bad Job Post Example
Job Title: Data Quality Analyst
Company: Global Data Group
Location: Nairobi
Job Type: Full-Time
Deadline: August 30, 2025
Job Summary
Global Data Group is hiring a Data Quality Analyst to review and validate datasets, identify data issues, and support the data team. The ideal candidate will be detail-oriented, experienced with databases, and able to work independently.
Key Responsibilities
- Perform routine data audits
- Ensure accuracy of data in reports
- Monitor data flow and integrity
- Work with data teams to resolve errors
Requirements
- Bachelor’s degree in data science, statistics, or related field
- 2–4 years of data experience
- Proficient in Excel and SQL
- Strong communication skills
How to Apply
Send your CV and cover letter to careers@globaldatagroup.com.
Only shortlisted candidates will be contacted.
❌ Why This Post Doesn’t Work (Breakdown)
1. The Job Title Is Generic and Uninspiring
“Data Quality Analyst” says what the role is—but not who it’s for or why it matters.
There’s no energy, no context, and no hook to draw in the right candidate.
2. The Introduction Lacks Purpose or Mission
There’s no explanation of what Global Data Group does. No insight into what problems the role solves.
A quality analyst wants to know why their work matters—not just that they’ll be doing “routine audits.”
3. Responsibilities Are Vague and Unimpactful
Phrases like “monitor data flow” or “ensure accuracy” are too general.
There’s no detail, no real-world examples, and no connection to business outcomes.
4. No Culture, Values, or Team Context
Who will the analyst work with? How do teams collaborate? What’s the work environment like?
There’s zero culture signal here—so top candidates assume it’s a black box or just another corporate silo.
5. Salary and Perks Are Omitted
No mention of compensation, leave days, benefits, or flexibility.
That’s a red flag for serious applicants—especially those who value transparency and trust.
6. The Application Process Feels Cold and Outdated
“Send your CV and cover letter” signals that the company hasn’t modernized their hiring process.
“Only shortlisted candidates will be contacted” is discouraging and dismissive—no one wants to be ghosted.
🔴 The Result?
Strong candidates keep scrolling.
You’re left with generic applications from people applying to every analyst job they can find—whether they’re qualified or not.
Bonus Tips to Make Your Job Post Stand Out
If you want to attract high-quality, motivated candidates—not just checkbox applicants—here are a few extra details you can add to your job post that most companies overlook.
These are the subtle trust-builders that serious applicants actually notice.
✅ 1. Add an IMPORTANT NOTICE About Candidate Privacy & Security
Reassure candidates that applying to your company is safe and legitimate. A short message can go a long way in building trust—especially in industries where job scams are common.
Example:
🔐 IMPORTANT: We take your privacy seriously. We will never ask for payment, banking details, or personal financial info at any point during the hiring process. All communication will come from an official company email.
✅ 2. Mention Leave Days or Flex Time
Candidates want to know they’re applying to a company that values rest and balance. Don’t leave this out—especially if your policy is better than the norm.
Example:
🌴 Enjoy up to 24 paid leave days per year, plus 5 additional flex days to recharge, travel, or just take a breather when you need it.
✅ 3. Highlight Training & Growth Opportunities
If you invest in your people, say so! Show candidates that this role leads somewhere—and that you’re committed to helping them grow.
Example:
📚 We offer a KES 60,000/year development budget for courses, conferences, and certifications. Whether you’re learning SQL or becoming a dbt expert, we’ll support your growth.
✅ 4. Add a Video from the Hiring Manager or Team
A 60–90 second Loom video can instantly humanize your job post. Let candidates see the person they’d report to, hear your tone, and feel your energy.
Example Intro Line in the Post:
🎥 Watch a short welcome from our Head of Data, Grace Kimani, on what success in this role looks like—and why data quality is central to everything we do.
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
These small touches don’t take much time—but they send a strong message:
📣 “We’ve put thought into this job post, and we’ll put thought into you as a candidate, too.”
Why You Shouldn’t Rely on AI to Write Your Job Post (Blindly)
Let’s be honest:
AI-generated job descriptions are everywhere now. Platforms like Manatal and Workable even offer one-click generators. It feels fast and efficient—but here’s the problem:
What AI gives you in speed, it takes away in substance.
❌ Here’s what happens when you rely on AI without guidance:
- You get generic, bland text that sounds like it was written by a robot.
- It misses your company’s tone, values, and voice.
- It creates vague responsibilities that could apply to any job.
- It often over-promises or under-sells what you actually need.
- And worst of all—it fails to attract high-quality candidates who want to work somewhere real, not templated.
Top talent doesn’t want to join a company that sounds like ChatGPT wrote the post in five seconds.
They want to feel a sense of purpose. They want to understand your mission, your tone, your people—and how they’ll fit in.
✅ The Better Approach: Use AI as a Collaborator, Not a Crutch
AI can be helpful—but only when you feed it the right raw materials.
Instead of asking:
“Write me a job description for a data analyst.”
Try this prompt:
“Help me write a job post for our company, Lumera Analytics. We’re hiring a Junior Data Quality Analyst to help with cleaning up datasets, learning SQL, and supporting the analytics team. Our culture is remote-friendly, growth-focused, and values clear communication. We offer a learning stipend, flex days off, and quarterly remote meetups. Here’s a rough draft of the intro and responsibilities I’ve written [paste notes here]…”
Then paste in your notes and let AI help you polish it—not invent it.
AI is like a smart assistant—it can clean up your writing, improve clarity, and organize your message.
But it can’t inject the heart of your company into the post unless you put it there first.
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, Culture-First Job Description
Job Title: Data Quality Analyst – Help Us Keep Our Data Clean, Fast & Reliable
Location: [Location] (Hybrid or Remote)
Type: [Job Type]
Salary: [Salary Range]
🎥 Watch a short video from our [Head of Data / Hiring Manager] on why data quality matters so much at [Company Name], and how this role fits into our mission.
[Insert Loom Link Here]
🏢 Who We Are
At [Company Name], we believe great decisions are powered by great data.
Whether we’re building dashboards, supporting product decisions, or delivering insights to clients, our goal is simple: keep the data clean, trustworthy, and actionable.
We’re a [size]-person [industry type] company working with [brief customer segment or product description], and we’re hiring a Data Quality Analyst to help us make sure that what we deliver is accurate from end to end.
💡 What You’ll Be Doing
- Monitor and clean datasets across multiple systems and teams
- Identify data quality issues and trace them to root causes
- Maintain data validation rules and documentation
- Work with data engineers to improve data pipelines
- Help analysts and stakeholders trust the data they use every day
🧠 What We’re Looking For
- 2–3 years of experience in a data quality, BI, or data operations role
- Strong SQL skills (e.g., joins, CTEs, data audits)
- Familiarity with cloud data warehouses (e.g., Snowflake, BigQuery, Redshift)
- Detail-oriented mindset—you catch inconsistencies others miss
- Bonus: experience with dbt, Great Expectations, or similar tools
⭐ Why This Role Is a Great Fit
This is a high-ownership role with real impact. You won’t be buried in bureaucracy or endless meetings. Instead, you’ll be the person who keeps our data sharp, our dashboards trusted, and our team moving fast with confidence.
If you’re someone who loves structure, accuracy, and solving the small problems that cause big issues—this role is made for you.
🎁 Perks & Benefits
- Health insurance + wellness stipend
- [#] paid leave days + public holidays
- Annual learning and certification budget
- One-time home office setup grant
- Quarterly team events or offsites
📥 How to Apply
We use WorkScreen.io to evaluate all applicants fairly and consistently. Start with a short skill-based assessment. If there’s a fit, we’ll invite you to a short call with the team. We respond to every application.
👉 [Insert WorkScreen Link]
✅ Option 2: Structured “Job Brief + Responsibilities + Requirements” Format
Job Title: Data Quality Analyst
Company: [Company Name]
Location: [Location]
Job Type: [Job Type]
Salary: [Salary Range]
🎥 Meet your future manager in this quick video: [Hiring Manager Name] shares what we’re building and how this role fits into the data operations team.
[Insert Loom Link Here]
📝 Job Brief
We’re hiring a Data Quality Analyst to help us maintain accurate, consistent, and trustworthy data across our analytics and reporting systems. This role supports teams across [Company Name] by proactively identifying data issues, improving validation processes, and collaborating with analysts and engineers.
🛠️ Responsibilities
- Perform routine data validation checks
- Clean and correct inconsistent or duplicate records
- Maintain clear documentation of data definitions and rules
- Support reporting accuracy by monitoring key datasets
- Collaborate with the engineering team on pipeline improvements
📌 Requirements
- Strong attention to detail and critical thinking
- Solid understanding of spreadsheets and basic database tools
- Working knowledge of SQL or a willingness to learn
- Clear communication skills
- Bonus: Familiarity with cloud-based data platforms or data quality frameworks
🎁 Perks & Benefits
- [#] days of paid leave
- Health insurance or wellness allowance
- Annual learning stipend
- Hybrid or fully remote flexibility
- Quarterly team check-ins or virtual events
📥 How to Apply
We use WorkScreen.io to keep our process structured and fair. Start by completing a short evaluation that gives us insight into your skills and attention to detail.
👉 [Insert WorkScreen Link]
We review every application and get back to all candidates—no résumé black holes.
Let WorkScreen Handle the Next Phase of Hiring
Writing a great job post is just the beginning.
Once the applications start rolling in, the real challenge begins:
How do you quickly identify the top candidates—without spending hours reading résumés or chasing vague answers in interviews?
That’s where WorkScreen.io comes in.
✅ WorkScreen helps you:
🔍 Automatically Identify Top Performers
WorkScreen evaluates every applicant through a short, role-specific test—no résumé bias, no guesswork.
Candidates are scored and ranked on a performance-based leaderboard, so you immediately see who’s actually qualified.
⚡ Save Time with One-Click Skill Tests
Easily send out practical evaluations that test the skills that actually matter for the role.
Whether you’re hiring a data analyst, customer support rep, or marketing assistant—WorkScreen helps you assess what candidates can do, not just what they claim.
🚫 Eliminate Low-Effort or AI-Generated Applications
WorkScreen filters out applicants who rely on AI tools, copy-paste responses, or one-click mass applications.
You spend less time with bots and time-wasters—and more time with real, thoughtful applicants who are genuinely interested in your role.
If you're tired of guessing who’s qualified—or worse, hiring the wrong person—let WorkScreen do the heavy lifting.
FAQ
A Data Analyst focuses on interpreting data to produce insights, reports, and visualizations that inform business decisions. Their job is to analyze trends, patterns, and opportunities using existing data.
A Data Quality Analyst, on the other hand, ensures that the data being used is accurate, clean, consistent, and reliable. They work behind the scenes to validate, clean, and structure data so that analysts, engineers, and decision-makers can trust what they’re using.
Think of it this way:
The Data Quality Analyst protects the integrity of the data.
The Data Analyst tells the story from that data.
Key skills include:
- Attention to detail: They must spot errors others miss.
- SQL proficiency: Especially for validating and querying large datasets.
- Experience with data quality tools: e.g., Great Expectations, Talend, dbt tests.
- Familiarity with modern data stacks: Snowflake, BigQuery, Redshift, etc.
- Communication: To explain data issues clearly to both technical and non-technical teams.
- Process thinking: They should be able to suggest improvements to prevent recurring data issues.
Soft skills like curiosity, accountability, and a problem-solving mindset are also critical.
This varies by region, experience level, and company size.
- In Kenya, a mid-level Data Quality Analyst typically earns between KES 100,000 and 180,000 per month.
- In the U.S., average salaries range from $65,000 to $95,000 per year, with higher-end roles going above six figures, especially in tech or finance.
Entry-level roles often start lower, but with experience and technical growth (especially SQL and data engineering skills), salaries can scale quickly.
Some of the most relevant tools include:
- SQL (must-have)
- Data warehouses like Snowflake, BigQuery, Redshift
- Data testing/validation frameworks: Great Expectations, dbt tests, Talend
- Spreadsheets (for entry-level tasks or audits)
- Documentation tools like Notion or Confluence (to keep rules and logic clear)
