Data Analytics Recruiter Job Description Template

Tatia Tatoshvili

Last updated on April 6, 2025 | 10 min read

Job Description TemplateData ScienceRecruitment

General overview of the role

A Data Analytics Recruiter specializes in identifying and hiring high-performing talent in the data analytics domain, including data scientists, analysts, engineers, and BI professionals. These recruiters serve as a crucial bridge between hiring managers and candidates, ensuring alignment in technical expertise, industry fit, and business goals. Success in this role requires a deep understanding of the analytics ecosystem, including statistical modeling, data platforms, and visualization tools. Whether supporting a startup or an enterprise, a strong Data Analytics Recruiter helps organizations harness data talent to drive strategic decisions and business outcomes.

Typical duties and responsibilities

• Develop tailored recruitment strategies for hiring data analysts, scientists, engineers, and BI experts across industries.
• Partner with hiring managers to refine job requirements and identify critical technical skills needed for each role.
• Source candidates from specialized platforms such as Kaggle, Stack Overflow, and data-focused LinkedIn groups.
• Conduct candidate screenings to evaluate proficiency in Python, R, SQL, Tableau, and statistical modeling techniques.
• Guide hiring teams through technical evaluations, including data challenge assessments or portfolio reviews.
• Stay up to date on analytics trends such as AI, machine learning, and data engineering best practices.
• Track and optimize key recruitment metrics including time-to-hire, cost-per-hire, and sourcing channel performance.
• Manage the full hiring process from job posting to offer negotiation and onboarding.

Required skills and experience

• 3+ years of experience recruiting for data analytics or data science roles.
• Solid understanding of analytics tools and programming languages such as Python, R, SQL, Tableau, and Power BI.
• Familiarity with cloud data environments and big data frameworks (e.g., AWS, Azure, Google Cloud, Hadoop).
• Experience evaluating candidates based on technical interviews, take-home assignments, or data portfolios.
• Strong written and verbal communication skills for collaborating with hiring teams and technical stakeholders.
• Knowledge of data compliance and privacy regulations (e.g., GDPR, HIPAA) relevant to analytics roles.
• Proven ability to manage multiple roles across technical teams and deliver timely results.
• Proficiency with sourcing and recruitment tools including ATS platforms, LinkedIn Recruiter, and niche data forums.

Nice to have/preferred skills and experience

• Experience recruiting for advanced analytics roles, such as ML engineers, NLP specialists, or AI researchers.
• Background in data science or business intelligence, allowing deeper candidate evaluations.
• Understanding of data storytelling and visualization best practices in analytics hiring.
• Familiarity with performance metrics like ROC-AUC, F1 score, or clustering accuracy when hiring for predictive modeling roles.
• Network of candidates from analytics bootcamps, hackathons, or academic institutions.

Data Analytics Recruiter salary

Salaries for a Data Analytics Recruiter vary based on experience, industry, and company size:

• Entry-level professionals typically earn between $60000 – $70000 annually.
• Mid-level recruiters can expect salaries between $70000 – $90000 annually.
• Senior-level Recruiters or those specializing in executive hiring may command salaries between $100000 or more annually.

How can I be a good Data Analytics Recruiter?

• Learn the fundamentals of data science and analytics, including common tools and frameworks.
• Stay current with industry trends, such as the adoption of machine learning and real-time analytics.
• Build a strong network within the data community, including forums, LinkedIn groups, and local meetups.
• Collaborate with hiring managers to create clear job descriptions that attract top-tier analytics talent.
• Ask thoughtful questions during technical screenings that uncover both hard and soft skills.

What are the Data Analytics Recruiter skills?

Hard skills

• Familiarity with analytics tools like Tableau, Power BI, and Python.
• Proficiency in sourcing tools and platforms (e.g., ATS, LinkedIn Recruiter, Kaggle).
• Understanding of data-related job functions and key performance indicators.
• Experience evaluating technical assignments and portfolios.

Soft skills

• Ability to communicate technical concepts with clarity.
• Analytical thinking to assess hiring data and recruitment performance.
• Adaptability to manage multiple hiring pipelines simultaneously.
• Strong organizational and prioritization abilities.

Why join us as a Machine Learning Architect?

• Work with data-driven companies that value innovation and measurable outcomes.
• Be at the forefront of hiring in one of the most in-demand fields globally.
• Gain access to cutting-edge tools and resources for recruitment and analytics assessment.
• Collaborate with cross-functional teams who rely on data to make critical decisions.
• Enjoy flexibility, competitive compensation, and a culture of continuous learning.

Quick Quiz: Are You Ready to Be a Data Analytics Recruiter?
1. How familiar are you with common data analytics roles and tools?

A. I have limited knowledge but am eager to learn.
B. I’m familiar but need a refresher.
C. I have deep knowledge and have been recruited for various analytics and data roles.

2. Which platforms are the most effective for sourcing data analytics talent?

A. Craigslist
B. LinkedIn, Kaggle, GitHub, and analytics-specific job boards
C. Pinterest

3. You need to hire a candidate for a business intelligence analyst role. What’s a key skill to assess?

A. Experience with SQL and data visualization tools like Tableau or Power BI
B. Ability to lift heavy objects
C. Familiarity with social media marketing tools

4. What tools should a Data Analytics Recruiter understand when evaluating technical candidates?

A. Tableau, Power BI, Python, SQL, and Google BigQuery
B. Adobe Photoshop and Illustrator
C. Construction software tools

5. What’s one current hiring trend in the data analytics industry?

A. Decreasing interest in data science careers
B. Increasing demand for data-driven decision-making and predictive analytics
C. Shift away from cloud-based data tools

Correct Answers
1. How familiar are you with common data analytics roles and tools?

C. I have deep knowledge and have been recruited for various analytics and data roles.

(An understanding of different data functions is key to successful recruitment in analytics).
2. Which platforms are the most effective for sourcing data analytics talent?