Top 12 SAS Programmer Skills to Put on Your Resume

Data is everywhere, and teams need people who can make sense of it fast. SAS programmers sit right in that stream, turning raw tables into answers and decisions. Showcasing the right skills on a resume isn’t fluff—it tells hiring managers you can clean, shape, analyze, and explain data without getting lost in the weeds.

SAS Programmer Skills

1. SAS Base
2. SAS Macro
3. SAS/STAT
4. SAS/GRAPH
5. SAS/SQL
6. SAS Enterprise Guide
7. SAS Visual Analytics
8. JMP
9. Data Step
10. PROC SQL
11. SAS/ACCESS
12. SAS Viya

1. SAS Base

Base SAS is the foundation. It’s where you read files, reshape messy inputs, join tables, summarize columns, and produce reports. DATA steps, procedures, formats, informats, logs—this is the day-to-day toolbox.

Why It's Important

Without Base SAS, nothing else stands. It’s the engine for data import, manipulation, analysis, and output, so a SAS programmer relies on it for nearly every task.

How to Improve SAS Base Skills

Level up with a steady rhythm of practice and pattern-spotting:

1. Master the fundamentals: DATA vs. PROC steps, the Program Data Vector (PDV), formats/informats, MERGE vs. JOIN, and the ODS system. Official SAS documentation is the gold standard reference.

2. Write code daily: Rebuild common data wrangling tasks from scratch. Try problems from data challenge sites to stretch your thinking.

3. Go deeper: Arrays, hash objects, indexes, user-defined formats, and efficiency options like COMPRESS—these pay dividends on large data.

4. Keep current: New releases add procedures, functions, and options. Track what changes and fold useful features into your habits.

5. Study style and structure: Small modules, readable naming, tight logs, and comments that explain intent. The Little SAS Book remains a tidy companion.

6. Peer reviews: Trade code with teammates. A second set of eyes catches inefficiencies and risky assumptions.

Consistency wins. Build muscle memory, then refine it.

How to Display SAS Base Skills on Your Resume

2. SAS Macro

Macro language stitches your programs together. It generates SAS code dynamically, parameterizes logic, and eliminates copy-paste repetition.

Why It's Important

Macros make pipelines flexible. One well-built macro can adapt to new inputs, datasets, or rules with a single parameter change.

How to Improve SAS Macro Skills

1. Own macro variables and scope: Local vs. global, timing of resolution, and quoting—tiny details, big impact.

2. Use macro functions: Text handling, conditionals, and iteration. Keep macro logic for code generation, not data processing.

3. Debug with intent: Options MPRINT, MLOGIC, and SYMBOLGEN, plus targeted %PUT statements. Clean logs, clear head.

4. Think modular: Small, composable macros with clear parameters and defaults. Reuse beats rewrite.

5. Optimize: Avoid unnecessary macro loops. Let DATA steps and procedures handle row-level work.

6. Document: Header blocks, parameter tables, examples, and edge-case notes. Future you will thank you.

7. Practice relentlessly: Build macro toolkits for common patterns—imports, QC checks, report shells.

Good macros feel invisible. They just make work lighter.

How to Display SAS Macro Skills on Your Resume

3. SAS/STAT

SAS/STAT bundles a vast set of procedures for modeling and inference—regression, survival, ANOVA, mixed models, categorical analysis, multivariate methods, and more.

Why It's Important

It turns curated data into tested hypotheses and defensible conclusions. In regulated spaces or research-heavy teams, this is core territory.

How to Improve SAS/STAT Skills

1. Reinforce the stats: Assumptions, diagnostics, interpretation. Don’t just run procedures—understand what they assert.

2. Use the right tool: Pick procedures deliberately—GLM vs. GENMOD vs. MIXED vs. PHREG—based on distribution and design.

3. Practice with real data: Public datasets, messy inputs, imperfect designs. Reality sharpens judgment.

4. Validate: Check fit, residuals, collinearity, and influence. Report uncertainty, not just point estimates.

5. Automate analysis flows: Macros that standardize model setups, output selection, and ODS reporting.

6. Stay current: New procedures and options appear regularly, especially in Viya-enabled environments.

Clarity in methods, clarity in results.

How to Display SAS/STAT Skills on Your Resume

4. SAS/GRAPH

SAS/GRAPH can build publication-ready visuals: charts, plots, and maps. While many teams now lean on ODS Graphics and the SG family of procedures (SGPLOT, SGPANEL, SGRENDER) for modern output, SAS/GRAPH still appears in long-lived codebases.

Why It's Important

Good graphics explain your analysis faster than paragraphs can. Whether you use SAS/GRAPH or ODS Graphics, you need clear, consistent, readable visuals.

How to Improve SAS/GRAPH Skills

1. Learn core procedures: PROC GPLOT, GCHART, GMAP if you inherit legacy scripts; add SGPLOT and SGPANEL for current workflows.

2. Master customization: Templates, styles, annotates, legends, and axes. Small tweaks change comprehension dramatically.

3. Standardize themes: Reusable templates to keep branding and readability consistent across projects.

4. Focus on purpose: Choose visuals based on the question—trend, comparison, distribution, relationship.

5. Benchmark output: Compare runtime and file sizes; tune ODS settings for web vs. print.

Visuals aren’t decoration. They’re the point of the spear.

How to Display SAS/GRAPH Skills on Your Resume

5. SAS/SQL

SAS and SQL meet in two places: knowing SQL concepts broadly (joins, set operations, windowing, subqueries) and applying them within SAS. Many teams casually say “SAS/SQL” to mean strong SQL skills used inside SAS.

Why It's Important

SQL fluency helps you think in sets, not loops. It speeds up joins, filters, aggregations, and reshaping, whether you run it in SAS or push queries down to databases.

How to Improve SAS/SQL Skills

1. Strengthen fundamentals: SELECT, WHERE, GROUP BY, HAVING, JOIN types, unions, and window functions.

2. Practice relentlessly: Rebuild data transformations in both DATA step and SQL to feel the trade-offs.

3. Push work to the source: When connected to databases, filter and aggregate upstream to cut data movement.

4. Think about indexes: Write predicates and joins that take advantage of available indexing.

5. Evolve your style: Name columns clearly, avoid ambiguous joins, and document assumptions right in the query.

6. Know when not to use SQL: Some row-by-row logic is simply cleaner in a DATA step.

SQL sharpens the edges of your data work inside SAS.

How to Display SAS/SQL Skills on Your Resume

6. SAS Enterprise Guide

SAS Enterprise Guide (EG) provides a point-and-click interface plus a code window for custom programs. Many organizations still run EG for SAS 9 environments, while SAS Studio is now the primary browser-based IDE, especially with Viya.

Why It's Important

It speeds up routine work, helps non-programmers run repeatable flows, and still lets programmers drop into code when nuance appears.

How to Improve SAS Enterprise Guide Skills

1. Explore power features: Process flows, prompts, tasks, custom tasks, reusable project templates.

2. Customize the workspace: Autocomplete, snippets, task defaults, and log preferences tuned to your workflow.

3. Automate: Schedule jobs, parameterize runs, generate consistent outputs with ODS templates.

4. Integrate: Blend EG with SAS Studio usage; where appropriate, pair with Python or R via supported bridges in your environment.

5. Stay updated: Track release notes for performance improvements and migration guidance to SAS Studio or Viya.

6. Learn from peers: User groups and internal communities surface quick wins and troubleshooting tricks.

Know EG, but be ready to pivot smoothly to SAS Studio when the platform shifts.

How to Display SAS Enterprise Guide Skills on Your Resume

7. SAS Visual Analytics

SAS Visual Analytics builds interactive dashboards and reports that invite exploration. Drag, drop, filter, drill. Tie visuals to data stories that stakeholders actually remember.

Why It's Important

It closes the loop. Analysts find insights; decision-makers need to see them quickly and act. Visual Analytics, especially on Viya, makes that handoff smooth.

How to Improve SAS Visual Analytics Skills

1. Start with design basics: Purpose-first layouts, consistent color, accessible labels, minimal clutter.

2. Practice on varied datasets: Wide, tall, sparse, dense. Different shapes of data expose different features.

3. Use parameters and controls: Prompts, sliders, actions, and linked objects to guide user journeys.

4. Blend with SAS code: Prep data in SAS Studio using robust, repeatable pipelines before visuals touch it.

5. Iterate with feedback: Ship drafts, watch how users interact, refine. Measure load times and responsiveness.

6. Keep current: New chart types, performance tweaks, export options—newer versions add helpful polish.

Dashboards that breathe. Reports that persuade.

How to Display SAS Visual Analytics Skills on Your Resume

8. JMP

JMP, from SAS, is built for interactive, exploratory analysis. Scientists and engineers love it for hands-on modeling, design of experiments, and visual discovery, while still complementing code-heavy SAS workflows.

Why It's Important

It shortens the distance between a question and an insight. With JMP’s dynamic visuals and JSL scripting, you prototype fast and formalize when you’re ready.

How to Improve JMP Skills

1. Learn the interface: Distributions, Graph Builder, Fit Y by X, Fit Model, and DOE. Click, drag, think out loud.

2. Pick up JSL: Automate repeated steps, parameterize analyses, package scripts for teammates.

3. Bridge with SAS: Move data and results between JMP and SAS when each tool has the edge.

4. Use the community: Example scripts, add-ins, and recipe-like solutions save hours.

5. Practice on real problems: Manufacturing, clinical QC, marketing tests—JMP shines where iteration matters.

6. Track new features: Updates routinely add modeling and visualization boosts.

Hands-on exploration today, scripted repeatability tomorrow.

How to Display JMP Skills on Your Resume

9. Data Step

The DATA step is SAS’s workhorse. Row-by-row logic, conditional transforms, merges, arrays, hash lookups, and the countless little moves that make datasets analysis-ready.

Why It's Important

It’s where you bend data to your will, safely and efficiently. Master it and you’ll outpace your workload.

How to Improve Data Step Skills

1. Trim I/O: KEEP= and DROP= options reduce variables in motion. Less to read, less to write.

2. Prefer WHERE over IF: WHERE filters before PDV population in many procedures, cutting I/O on large tables.

3. Lean on indexes: Index keys used in WHERE or BY steps can be a game changer on big joins and lookups.

4. Compress when right: COMPRESS=YES trades CPU for smaller storage and faster disk movement. Measure to confirm.

5. Use hash objects for lookups: In-memory joins beat sorts and merges when the reference set is modest.

6. Avoid unnecessary sorts: Many procedures accept BY statements without a physical sort if indexes or order suffice.

7. Arrays for repetition: Clean up repetitive logic and reduce code bulk with arrays.

8. Macro constants: Store repeated constants in macro variables to centralize and avoid typos.

9. NOBS and point control: Use NOBS= and POINT= for precise control when needed.

10. Read the log like a hawk: Notes about automatic conversions, truncation, or lost card lines hint at lurking bugs.

Fast, tidy, and predictable. That’s the Data Step sweet spot.

How to Display Data Step Skills on Your Resume

10. PROC SQL

PROC SQL brings SQL straight into SAS. Create tables, join datasets, summarize, pivot, and more—often in fewer lines than equivalent DATA step flows.

Why It's Important

It’s a versatile alternative to DATA steps and merges, and it mirrors how databases think. For mixed SAS–database environments, this alignment is powerful.

How to Improve PROC SQL Skills

1. Solidify SQL: Core syntax, join strategies, grouping logic, set operations, and window functions.

2. Use SAS-specific features: Dictionary tables, creating indexes inline, macro-driven query templates, and pass-through SQL.

3. Optimize: Minimize Cartesian joins, select only needed columns, push predicates to the source, and favor indexed joins.

4. Structure for clarity: Indent joins, alias consistently, and name columns explicitly to dodge ambiguity.

5. Test and refactor: Compare PROC SQL and DATA step for performance and readability. Keep the simpler one.

6. Handle wide tables carefully: Explicit column lists beat SELECT * in production code.

Write queries that read clean and run lean.

How to Display PROC SQL Skills on Your Resume

11. SAS/ACCESS

SAS/ACCESS connects SAS to external data sources—relational databases, cloud warehouses, Hadoop-era systems, and more—so you can read and write data without awkward exports.

Why It's Important

It keeps work close to the data. Push filters and aggregations down, avoid bloated extracts, and respect database strengths.

How to Improve SAS/ACCESS Skills

1. Know your target system: Each database has quirks—data types, functions, optimizer behavior, and bulk-load options.

2. LIBNAME well: Map libraries clearly, tune options for read/write performance, and use views to encapsulate logic.

3. Use SQL pass-through: Send native SQL directly when it’s faster or more expressive than PROC SQL’s implicit pass-down.

4. Reduce data movement: Filter early, select only required columns, summarize upstream, and paginate when needed.

5. Exploit indexes and keys: Align joins and filters with indexed columns; avoid functions that block index use.

6. Consider parallelism: Where supported, enable multi-threaded reads/writes and bulk-loads for big jobs.

7. Mind the network: Throughput and latency matter. Coordinate with IT on connectivity and drivers.

8. Train and document: Capture connection patterns, credentials handling, and performance tips in team playbooks.

Smart connections are half the performance battle.

How to Display SAS/ACCESS Skills on Your Resume

12. SAS Viya

SAS Viya is the cloud-native evolution of the platform. It brings scalable, distributed analytics with CAS (Cloud Analytic Services), tight integration with SAS Studio, and smooth interoperability with Python and R.

Why It's Important

Modern teams span tools and languages. Viya scales workloads, lets you mix ecosystems, and shortens deployment paths for analytics and AI.

How to Improve SAS Viya Skills

1. Understand CAS: Loading, partitioning, persistence, and actions. Treat memory and distribution as first-class concerns.

2. Adopt Viya-native procedures: Learn the Viya equivalents and new actions that replace or extend SAS 9 procedures.

3. Work in SAS Studio: Projects, snippets, tasks, and integrated versioning—your daily cockpit on Viya.

4. Blend languages: Use SWAT or REST endpoints to connect from Python or R; build repeatable pipelines spanning tools.

5. Explore ML: Gradient boosting, forest methods, neural networks, model governance, and fair/robust modeling options.

6. Practice on real scale: Push biggish data, test concurrency, and observe resource usage to learn the system’s personality.

7. Track releases: Viya 4 evolves quickly—new actions and performance tweaks arrive often.

8. Invest in training: Formal courses plus hands-on labs dramatically accelerate adoption.

Cloud-first analytics changes how you build, share, and ship insights.

How to Display SAS Viya Skills on Your Resume