ATS systems at tech companies don't score resumes the same way non-tech recruiters do. They're looking for specific tools, languages, frameworks, and methodologies — and they care about exact terminology, not paraphrases. 'Experience with databases' will not match a query for 'PostgreSQL'.
Tech roles are among the most ATS-filtered positions in the market. Companies like Google, Amazon, and Meta process millions of applications annually. Without the right keyword signals, your resume is invisible — regardless of your actual skill level.
How ATS keyword matching works in tech hiring
Technical ATS systems (Workday, Greenhouse, Lever, iCIMS) parse your resume and compare it against a scored list of required and preferred skills from the job description. The recruiter sees a match percentage. Higher match = higher in the stack.
Software Engineering: core ATS keywords
- Languages: Python, JavaScript, TypeScript, Java, Go, C++, Rust, Kotlin, Swift, Ruby, Scala
- Frontend: React, Next.js, Vue.js, Angular, HTML5, CSS3, Tailwind, Redux
- Backend: Node.js, Express, FastAPI, Django, Spring Boot, Rails, gRPC
- Cloud: AWS, GCP, Azure, Lambda, EC2, S3, Cloud Functions, Azure DevOps
- DevOps/Infra: Docker, Kubernetes, Terraform, CI/CD, GitHub Actions, Jenkins
- Databases: PostgreSQL, MySQL, MongoDB, Redis, DynamoDB, Elasticsearch, Cassandra
- Practices: microservices, REST API, GraphQL, TDD, system design, code review, Agile
Data Engineering and Analytics: core ATS keywords
- Languages and query: Python, SQL, Spark, Scala, dbt
- Warehouses: Snowflake, BigQuery, Redshift, Databricks
- Pipelines: Airflow, Kafka, Fivetran, dbt, Stitch, Glue
- Visualisation: Tableau, Power BI, Looker, Metabase
- Concepts: ETL, data modelling, data governance, data quality, real-time streaming, batch processing
Product Management: core ATS keywords
- product roadmap, product strategy, go-to-market, product discovery
- Jira, Confluence, Notion, Figma, ProductBoard, Miro
- Amplitude, Mixpanel, Segment, Google Analytics, A/B testing
- OKRs, KPIs, user research, user stories, acceptance criteria
- stakeholder management, cross-functional, sprint planning, backlog prioritisation
Machine Learning and AI: core ATS keywords
- Python, TensorFlow, PyTorch, scikit-learn, Keras, Hugging Face
- NLP, computer vision, large language models (LLMs), transformer models
- MLOps, model deployment, model monitoring, feature engineering
- AWS SageMaker, GCP Vertex AI, Azure ML, MLflow, Kubeflow
- statistical modelling, regression, classification, deep learning, reinforcement learning
The keyword placement rule
Keywords should appear in at least two places to signal genuine experience: once in your Skills section (as a named tool or technology) and once in your experience bullets (in context, showing how you used it and what outcome it produced). Keyword stuffing in a footer or hidden text no longer works — modern ATS systems score contextual relevance.
HireSprint extracts the exact keywords from any tech job description and shows you which ones are missing from your resume — sorted by importance. One click tailors your skills section and rewrites relevant bullets to match.
