The Resume Checklist For: Data Analysts, Data Engineers, Data Scientists

Former data analyst here, now working as a recruiter specialised data & analytics roles.

I've screened thousands of resumes for Data Engineers, Data Analysts and Data Scientists roles.

At request (shoutout Dale Nelson ) here's the answer to:

Do you have a resume check-list or something of what businesses are looking for per chance [in the data & analytics field]?

Resume Structure

Let's square away the base structure of resume formatting first. This can vary slightly from person-to-person and often there's no real big points deducted if you stray slightly away from the general guidelines however there are some basics to adhere to.

Here are the quick-fire criteria that sets the foundation:

Note; this is for Australia - different cultures have different best practices.

Sections to Include:

(and typically in this order)

• Summary – A short, sharp intro about who you are and what you bring. You can add a little bit of flair here to differentiate yourself and give some personality. Ideally, each summary is written to each role you apply for to make it more relevant.
• Education – Formal qualifications typically from a tertiary institution such as a university or college. This would include graduate certificates, degrees, masters and doctorates. If you have a notable achievement e.g. high course-weighted average (CWA), then put it here as a sub point. If you have a poor CWA, I'd leave it off!
• Certifications – Especially Snowflake, AWS, Microsoft, Azure or Databricks. Can also include relevant certifications from online providers such as Udemy, Coursera or DataCamp. Sometimes the online course list can get very extensive so be wary of not making this too long e.g. 10+. For example; don't include the 'R Fundamentals' course you did 8 years ago for a Python-role.
• Technical Skills – List key technologies you’ve used (relevant to the job you want). The same with certifications, if you've had a long career this list might be a page long so keeping to the role requirements is best (more information below about typically techstack wanted for popular job titles).
• Soft Skills – These are often overlooked but can make a real difference. Think about problem-solving, communication, stakeholder management and business acumen. The ability to translate complex technical ideas into business-friendly language is highly valued. Instead of going generic e.g. 'communication', try get specific on areas you used soft skills.
• Achievements – This is your chance to show impact. Where possible, quantify your contributions (e.g., "Improved dashboard load time by 50%", "Reduced ETL processing time by 30%"). Focus on results, not just responsibilities.
• Experience – Detail your key projects, responsibilities, and contributions. Stick to clear, concise bullet points that highlight what you did, how you did it and the impact it had. If you’ve worked in multiple industries, highlight transferable skills. If there's tangible outcomes, let us know!
• Projects – These are especially useful if you have limited work experience. Include personal or open-source projects that demonstrate your skills. If relevant, link to your GitHub, portfolio or online dashboards. Highlight the problem solved, technologies used, the impact and the WHY.

Links to Include:

• GitHub – Especially for Data Engineers and Data Scientists.
• Portfolio/Dashboards – If you’ve built dashboards, models, or reports link to them.
• LinkedIn – Keep it updated!

Length: 2-3 pages. One page is often too short, unless you able to use all the space efficently.

Role-Specific Tips

Great, now we have the basics out the way (and something that there is a lot of public information on if you want to dive deeper), let's get into the role specifics:

Data Analyst

Whilst Data Analyst duties vary from role to role generally it business problem being solved is having data visibility to make data-driven decisions. That typically means you need to understand the business data, visualise that data and communicate it effectively to stakeholders

• Key Words: Dashboarding, data visualisation, insights & reporting, business intelligence, data cleaning, data quality, data storytelling, stakeholder management, requirements gathering, business impact.
• Technical Skills: SQL, Excel, Power BI, Tableau, Looker, Python (pandas, seaborn, matplotlib), data warehouses (SQL Server, Snowflake, Redshift).
• Soft Skills: Strong communication, ability to translate technical insights into business terms, critical thinking, attention to detail, problem-solving, stakeholder engagement and adaptability.

Data Engineer

Data Engineers build and maintain the infrastructure that enables data analysis. Their job is to ensure that data is accessible, clean and efficiently processed for analytics and machine learning models. They design ETL pipelines, optimise data storage and integrate various systems to ensure smooth data flow.

• Key Words: ETL, data pipelines, automation, data modelling, cloud, big data, streaming, orchestration, infrastructure, API integration, CI/CD.
• Technical Skills: SQL, Python, Spark, Airflow, cloud services (AWS/GCP/Azure), Terraform, Kubernetes, dbt, Apache Kafka, Redshift, Snowflake, BigQuery, Databricks.
• Soft Skills: Problem-solving, collaboration with analysts and data scientists, scalability thinking, troubleshooting, system design and process automation mindset.

Data Scientist

Data Scientists extract insights from data, build predictive models and apply statistical methods to solve business problems. They work closely with data engineers to access the right data and analysts to translate findings into actionable insights.

• Key Words: Machine learning, AI, predictive analytics, deep learning, statistics, NLP, optimisation, A/B testing, feature engineering and popular data science models e.g. random forest, ball tree etc.
• Technical Skills: Python (scikit-learn, TensorFlow, PyTorch), SQL, cloud ML services (AWS SageMaker, Azure ML), Spark, Databricks, statistical modelling, hypothesis testing.
• Soft Skills: Analytical thinking, communication of technical concepts to non-technical audiences, experimental mindset, business impact focus and adaptability. Key here is being able to link data science models to the business outcome.

Final Note

These are some great tips and tricks but the Golden Rule?

Tailor your resume to the job description.

A generic resume won’t cut it—employers (and recruiters) want to see how your skills and experience align with their specific needs. Please make it easy for us!