Skills to put on a data resume

A data resume is read in two passes: the tools first (SQL, Python, the team's BI tool or cloud), then what you got out of them. An analysis is only worth the decision it informed: that is what recruiters look for in your experience.

Pick your angle for the role: a data analyst highlights BI and reporting, a data engineer pipelines and data quality, a data scientist modeling and putting models into production. Write each tool the way job ads write it, so the recruiter and the applicant tracking system make the connection.

Technical skills

Languages and data wrangling

• SQL The skill every data role shares: joins, aggregations, window functions.
• Python The language of analysis and machine learning: name the libraries you used.
• Pandas The go-to library for preparing and exploring data in Python.
• NumPy Numerical computing in Python, often listed with Pandas.
• R Still widely used in statistics, research and healthcare.
• Excel Still requested of data analysts: pivot tables, XLOOKUP, Power Query.

Visualization and BI

• Power BI A BI tool often requested in job ads: name the dashboards you delivered.
• Tableau Common in large organizations: say who used your dashboards.
• Looker Common in companies on Google Cloud, often with BigQuery.
• Data visualization Choosing the right chart for the message: show it with a report used by leadership.
• Google Analytics For web and marketing analytics roles: GA4 is the current version.

Data engineering

• ETL Extract, transform, load (or ELT): describe the pipeline, its volume and frequency.
• Airflow A widely used pipeline orchestrator.
• dbt Versioned, tested SQL transformations: at the heart of the analytics engineer role.
• Spark Processing large volumes; PySpark is its Python form.
• Kafka Real-time data streams: say what the use case was.
• Snowflake A widely used cloud data warehouse.
• BigQuery Google Cloud's data warehouse.
• Data modeling Star schemas, fact and dimension tables: what makes data usable by everyone.

Statistics and machine learning

• Statistics Hypothesis testing, regression, confidence intervals: the basis that separates analysis from mere description.
• Machine learning Say what problem the model solved and how you evaluated it.
• scikit-learn The standard library for classic machine learning in Python.
• Deep learning Keep it for real neural network projects, with TensorFlow or PyTorch.
• PyTorch A deep learning framework widely used in research and in industry.
• NLP Natural language processing: text classification, information extraction.
• A/B testing Designing and reading a controlled experiment: much sought after in product and marketing teams.

Cloud, tooling and governance

• Git Versioning your queries and notebooks: a sign of maturity for a data profile.
• Docker Useful as soon as a model or pipeline goes into production.
• AWS Name the services (S3, Redshift, Glue) in your experience.
• Azure Common alongside Power BI and the Microsoft ecosystem (Data Factory, Fabric).
• Data quality Checks, tests, documentation: the skill that prevents bad decisions.
• Data privacy (GDPR, CCPA) Knowing what you may do with personal data: expected whenever you handle customer data.

Soft skills

Soft skills are proven with facts: each one below with how to show it.

• Analytical thinking Starting from a business question and turning it into queries and metrics.
• Data storytelling Explaining a result to leadership without jargon: often what makes the difference in interviews.
• Attention to detail Checking your numbers before presenting them: tell the story of a data error you caught.
• Business acumen Understanding the revenue, margin or customer journey behind the data.
• Critical thinking Questioning a correlation, a sample, a source: the reflex that protects decisions.
• Stakeholder management Taking a vague request and turning it into a dashboard people actually use.
• Curiosity New tools, new methods: a field that changes fast.

Add these skills to my resume

Click the ones you really have: they join the resume kept in this browser by the resume builder, with no AI and nothing sent. Without a resume, the first click creates it.

Recognized certifications and qualifications

Put them in the Education or Certifications section of your resume, with the year, only if you hold them.

• Microsoft Certified: Power BI Data Analyst Associate Microsoft certification on Power BI (exam PL-300).
• Google Data Analytics Professional Certificate An online program from Google covering spreadsheets, SQL and Tableau, aimed at beginners.
• Google Cloud Professional Data Engineer Google Cloud certification on designing data processing systems.
• AWS Certified Data Engineer – Associate Amazon Web Services certification on data pipelines.
• Databricks Certified Data Engineer Associate Databricks certification on Spark and its platform.
• SnowPro Core Certification Snowflake's foundation-level certification.
• Data Analyst Level 4 Apprenticeship An apprenticeship standard in England: list it in your education with the provider's name.

Where to put them on your resume

Keep a grouped Skills section (languages, BI, engineering, statistics), then repeat each tool in the job where you used it: "Built a Power BI sales dashboard on dbt models in Snowflake, used every week by…".

For a data scientist, one well-described project beats a list of algorithms: the problem, the data, the model, how you measured its quality and what it changed. For a first role, your coursework or portfolio projects play that part (see the guide to a resume with no experience).

Tailor the top of your resume to each job: a data analyst role and a data engineer role do not look for the same words. The guide to tailoring your resume to a job explains the method, and the resume / job match checker shows what is missing.

Example bullet points for your experience

Fictitious examples, built with the XYZ formula: replace them with your own results, and never make up the measure.

• Before Created dashboards.

  After Replaced twelve weekly Excel files with a Power BI sales dashboard used by twenty branch managers, freeing half a day of reporting each week.

• Before Maintained data pipelines.

  After Brought sales data freshness from next day to under an hour by migrating nightly batch jobs to Airflow and dbt.

• Before Churn prediction model.

  After Targeted the retention campaign at the highest-risk customers with a scikit-learn model, lifting the renewal rate as measured by an A/B test.

• Before Ad hoc analysis for marketing.

  After Showed that half the acquisition budget went to a low-return channel by joining campaign and order data in SQL, and got it reallocated.

Common mistakes

• Listing algorithms without a project. "Random forest, XGBoost, neural networks" does not say what you can do. A project described with its result does.
• Leaving out the impact of the analysis. An analysis is judged by the decision it enabled: say who used it and what changed.
• The same resume for analyst, engineer and scientist roles. The three roles do not hire on the same tools: move the target role's tools to the top.
• Confidential or made-up figures. Give orders of magnitude or relative changes when amounts are confidential, but never a measure you could not back up.
• Neglecting communication. Data job ads very often ask you to present findings: show a presentation, a leadership meeting, documentation.

Frequently asked questions