Top 12 SAS Developer Skills to Put on Your Resume

Hiring managers want proof, not fluff. SAS developers who mix sharp programming chops with analytical instinct rise faster, ship cleaner projects, and don’t get stuck wrestling the same data headaches week after week. Build the right stack of skills and you’ll have options—across industries, across tools, across teams.

SAS Developer 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. SAS Data Integration Studio
9. SAS Viya
10. Hadoop Integration
11. Python (for SASPy)
12. R (for integration)

1. SAS Base

SAS Base is the foundation: data steps, procedures, formats, informats, ODS output, and the nuts-and-bolts that move data from messy to model-ready. It spans access, manipulation, analysis, and reporting across flat files, databases, and more.

Why It's Important

SAS Base powers everything else. Clean joins, robust data validation, efficient processing—this is where reliability is built and where time is saved.

How to Improve SAS Base Skills

Level up with steady practice and a bias for tidy, fast code:

1. Master core building blocks: Data step logic, merges, BY-group processing, arrays, hash objects, ODS. Know when to reach for each.

2. Write for performance: Keep passes over data low, index with intent, use WHERE over IF when it matters, profile big jobs.

3. Lean on documentation: Procedure options and data set options hide speed-ups. Read the fine print.

4. Practice with real datasets: Public data, internal sandboxes—anything messy enough to teach you something.

5. Track changes in SAS 9 and Viya: New features land often. Note syntax differences and new procedures.

6. Certify if it helps your goals: Structure your study, validate your baseline, then keep building.

7. Hands-on access: For learning, use SAS OnDemand for Academics to practice without local installs.

Consistency wins. Small daily reps beat occasional marathons.

How to Display SAS Base Skills on Your Resume

2. SAS Macro

The SAS Macro facility lets you parameterize programs, generate code, and automate repetitive logic. Less copy-paste. More consistency. Faster iterations.

Why It's Important

Macros shrink sprawling programs and make pipelines adjustable without surgery. Good macro design speeds delivery and cuts defects.

How to Improve SAS Macro Skills

1. Know scope cold: Local vs global variables, macro variable resolution, quoting. This avoids mysterious bugs.

2. Use macro functions: Text handling, conditional branching, loops—use them to drive dynamic logic, not to replace data step work.

3. Keep loops lean: Generate only what you need. Push data work to procedures or data steps.

4. Debug deliberately: %PUT, MPRINT, SYMBOLGEN, MLOGIC. Turn them on, read the log, fix the root.

5. Modularize: Build small, reusable macros. Clear parameters. Defaults that make sense.

6. Style and safety: Naming conventions, comments, input validation. Your future self will thank you.

7. Review often: Refactor macros as requirements evolve. Avoid macro creep.

Great macros feel invisible. They do the heavy lifting quietly.

How to Display SAS Macro Skills on Your Resume

3. SAS/STAT

SAS/STAT is the toolbox for statistical modeling and inference: regression families, survival analysis, mixed models, Bayesian methods, experimental design, and more.

Why It's Important

When decisions ride on numbers, you need methods you can trust. SAS/STAT brings breadth, depth, and battle-tested procedures that teams can audit and reproduce.

How to Improve SAS/STAT Skills

1. Ground yourself in theory: Model assumptions, diagnostics, interpretation. Procedures are only as good as the thinking behind them.

2. Map problems to procedures: GLM vs GENMOD vs MIXED vs GLIMMIX, PHREG vs LIFETEST—choose with intention.

3. Use ODS for output: Capture results as data sets, validate programmatically, and feed downstream reports.

4. Practice on varied data: Small and clean, large and gnarly. Simulate when needed to learn edge cases.

5. Stay current: New options and procedures arrive regularly; skim release notes and experiment.

6. Read examples: Replicate canonical analyses, then twist them to fit your use cases.

Clarity in modeling beats novelty. Every time.

How to Display SAS/STAT Skills on Your Resume

4. SAS/GRAPH

SAS/GRAPH and modern ODS Graphics give you publication-grade visuals from within SAS. Today, SGPLOT, SGRENDER, and the Graph Template Language do most of the heavy lifting; older PROC GPLOT and GCHART are legacy.

Why It's Important

Sharp visuals cut through noise. Good charts make analysis land with non-technical audiences and speed decisions.

How to Improve SAS/GRAPH Skills

1. Prioritize SG procedures: Learn SGPLOT/SGRENDER first. They’re flexible, consistent, and align with ODS.

2. Adopt GTL: Create reusable templates, control layout and styling, and standardize look-and-feel across reports.

3. Annotate when necessary: Add markers, lines, and notes with precision—only when it truly adds clarity.

4. Prep data for plotting: Summarize upstream. Avoid over-plotting. Keep axes honest.

5. Design rules matter: Label cleanly, avoid chartjunk, choose scales wisely, focus the eye.

6. Iterate with feedback: Share drafts, watch how people read them, then trim or refine.

Make the story obvious. Let the chart breathe.

How to Display SAS/GRAPH Skills on Your Resume

5. SAS/SQL

SAS/SQL (PROC SQL) blends SQL with SAS. Join, aggregate, filter, create views, and manage metadata without leaving the SAS environment.

Why It's Important

Real-world data lives in relational stores. PROC SQL lets you query at scale, push work into databases, and keep pipelines simpler.

How to Improve SAS/SQL Skills

1. Master joins and set ops: INNER, LEFT, FULL, UNION, INTERSECT. Understand nulls, duplicates, and join selectivity.

2. Know when to choose DATA step: PROC SQL is elegant; DATA steps can be faster or clearer for row-wise logic. Pick the right tool.

3. Optimize queries: Index smartly, filter early, project fewer columns, avoid Cartesian traps.

4. Practice consistently: Daily SQL challenges, refactoring old queries, and reviewing explain plans where supported.

5. Leverage pass-through: Push compute to the database when possible to reduce data movement.

6. Harden code: Validate row counts, check join keys, assert assumptions in logs.

Fast, correct, readable. Aim for all three.

How to Display SAS/SQL Skills on Your Resume

6. SAS Enterprise Guide

SAS Enterprise Guide (EG) is a Windows-based IDE with tasks, flows, and code nodes. It suits analysts who like point-and-click plus code, and teams standardizing repeatable workflows.

Why It's Important

EG accelerates common tasks, automates routine jobs, and helps non-coders contribute while still allowing full SAS programming.

How to Improve SAS Enterprise Guide Skills

1. Speed up navigation: Learn shortcuts, favorite tasks, and quick filters.

2. Exploit tasks: Use tasks to prototype quickly, then peek at the generated code to learn and refine.

3. Customize the workspace: Pane layouts, log options, default libraries—tune it to your flow.

4. Automate with macros and prompts: Parameterize projects so they can run hands-free across environments.

5. Tune performance: Reduce data movement, cache intermediate results wisely, and keep project trees tidy.

6. Know the ecosystem: Many teams are moving workflows to SAS Studio and Viya; understand portability.

7. Stay updated: New EG releases add stability and features; keep an eye on release notes.

Build flows that survive handoffs and audits.

How to Display SAS Enterprise Guide Skills on Your Resume

7. SAS Visual Analytics

SAS Visual Analytics (VA) turns data into interactive dashboards and reports, with forecasting, decision trees, and rich filtering baked in—especially powerful on Viya.

Why It's Important

Stakeholders need answers they can explore. VA packages analysis into shareable, interactive views that people actually use.

How to Improve SAS Visual Analytics Skills

1. Design with intent: Start with the questions. Select visuals that match the decision, not the novelty.

2. Prepare data upstream: Model, clean, and summarize before VA. Fewer transformations at render time, faster dashboards.

3. Use advanced analytics: Add forecasts, clustering, and decision trees when they add signal; explain them clearly.

4. Make it interactive: Parameters, prompts, linked objects, drill-throughs—let users explore without getting lost.

5. Standardize styles: Themes, color palettes, and consistent labeling build trust and speed comprehension.

6. Test performance: Optimize data sources, limit high-cardinality visuals, and monitor load times.

7. Keep current: New VA releases on Viya ship often; adopt features that simplify builds.

Clarity first. Flair later.

How to Display SAS Visual Analytics Skills on Your Resume

8. SAS Data Integration Studio

SAS Data Integration Studio is a visual ETL environment for designing, scheduling, and governing data flows across varied sources and targets.

Why It's Important

It standardizes pipelines, enforces lineage, and shortens the path from source to warehouse to analytics—without scattering hand-written scripts everywhere.

How to Improve SAS Data Integration Studio Skills

1. Master core transforms: Joins, sorts, lookups, error handling, and metadata bindings.

2. Model for governance: Clear naming, versioning, lineage docs, and repeatable job patterns.

3. Build for operations: Parameterize environments, add robust logging, and design restartable jobs.

4. Tune performance: Push compute to databases, partition smartly, reduce I/O, and parallelize where safe.

5. Learn advanced features: Custom transforms, dependency management, and job scheduling at scale.

6. Watch the roadmap: Many teams are adopting SAS Studio Flows and Viya-native data flows; plan for coexistence and migration.

Good ETL is invisible when it’s working. Aim for boring reliability.

How to Display SAS Data Integration Studio Skills on Your Resume

9. SAS Viya

SAS Viya is a cloud-ready, in-memory analytics platform powered by CAS. It blends SAS procedures with open-source access (Python, R), modern APIs, and collaborative tooling.

Why It's Important

Viya scales models and data prep across big hardware without clunky handoffs. You can deploy analytics faster, integrate with modern stacks, and keep governance intact.

How to Improve SAS Viya Skills

1. Understand the architecture: CAS servers, workers, libraries, and how data is loaded, persisted, and promoted.

2. Program on Viya: Use SAS procedures optimized for CAS, and know when to run on CAS vs the compute server.

3. Adopt REST APIs: Automate jobs, manage content, and integrate Viya into CI/CD pipelines.

4. Work cross-language: Drive CAS from Python or R when it makes sense; keep data locality in mind.

5. Use Viya for learning: SAS OnDemand and learner programs provide hands-on access without heavy setup.

6. Track releases: Viya evolves quickly—read what’s new, try features early, retire old patterns.

7. Build real projects: Small end-to-end flows: ingest, prep, model, deploy, monitor. Repeat with variations.

Think platform, not scripts. That’s the unlock.

How to Display SAS Viya Skills on Your Resume

10. Hadoop Integration

Hadoop integration means connecting SAS to distributed storage and compute for biggish data—historically HDFS and YARN, increasingly Spark and cloud object stores alongside, often through SAS/ACCESS and in-database techniques.

Why It's Important

Big, wide, and raw data still shows up in clusters and data lakes. Strong integration lets you push work to where the data lives and skip slow exports.

How to Improve Hadoop Integration Skills

1. Use SAS/ACCESS effectively: Configure secure, performant connections. Push predicates and joins down when possible.

2. Prefer in-cluster compute: Run analytics where data sits—Hadoop or Spark—so you move fewer bytes.

3. Tune storage and formats: Parquet/ORC, partitioning, compression. Design tables for analytics, not just ingestion.

4. Harden governance: Kerberos, Ranger/Sentry or equivalent access controls, and auditable data movement.

5. Plan for the modern stack: Many orgs are shifting to Spark and cloud warehouses. Build skills that transfer.

6. Monitor end-to-end: Job logs, resource usage, and SLA checks—catch hotspots early.

Move code to data, not data to code—whenever you can.

How to Display Hadoop Integration Skills on Your Resume

11. Python (for SASPy)

SASPy lets Python drive SAS sessions, move data to and from pandas, and run procedures—great for mixing SAS strengths with Python’s rich ecosystem.

Why It's Important

You keep SAS’s reliability and add Python’s flexibility. More plotting, more feature engineering, more automation options.

How to Improve Python (for SASPy) Skills

1. Get fluent in Python: Core syntax, functions, virtual environments, and packaging so your setup doesn’t wobble.

2. Master pandas: Indexing, groupby, joins, reshaping. It’s the bridge for SAS data sets.

3. Configure SASPy cleanly: Stable connections, consistent auth, and reliable I/O between SAS and Python.

4. Start simple: Run a PROC from Python, pull results, then scale up to parameterized jobs and notebooks.

5. Handle data at scale: Chunking, efficient transfers, and avoiding unnecessary conversions.

6. Work in notebooks: Jupyter for exploration, then move to scripts for productionized tasks.

Pick the right engine for each step, then stitch them together.

How to Display Python (for SASPy) Skills on Your Resume

12. R (for integration)

R brings powerful stats and graphics that pair well with SAS. With the right setup (for example, RLANG and SAS/IML where applicable), you can weave R code into SAS workflows and exchange data cleanly.

Why It's Important

Some methods and visual idioms live first in R. Integrating it lets you deliver niche analyses without abandoning SAS governance.

How to Improve R (for integration) Skills

1. Nail the basics: Vectors, data frames, functions, and the tidyverse way of thinking.

2. Learn fast data wrangling: dplyr or data.table for joins, reshapes, and grouped ops at speed.

3. Plan the integration: Understand how to pass data between SAS and R, how to log, and how to secure execution.

4. Report with RMarkdown: Reproducible, shareable documents that slot into existing reporting flows.

5. Add interactivity: Shiny apps for exploratory needs—lightweight, focused, and reliable.

6. Mind policies: Many environments restrict external engines; coordinate with admins early.

7. Practice on real tasks: Pick a SAS report, add an R visual or model, measure the impact, iterate.

Blend tools. Keep results reproducible. Document the handoffs.

How to Display R (for integration) Skills on Your Resume