Guardare
Senior Python Data Engineer / Applied AI Engineer
- Compensation
- Not disclosed
- Location
- United States
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Not specified
- Experience
- Not specified
- Category
- Data engineering
- Posted
Skills
Must have
- A Bachelor’s degree in Engineering is required, though a Master’s degree is preferred.
- 5 + years of relevant professional experience in data engineering.
- Strong software engineering fundamentals: clean code, testing, debugging, version control, and maintainable production systems.
- Engineering, quantitative, or technical background with strong professional software development experience.
- Strong experience working with APIs, JSON, data transformation, and backend data pipelines.
- Strong Python experience, with broader programming experience in other languages welcome.
- Solid SQL skills, ideally with PostgreSQL.
- Experience with ETL/ELT workflows and production data processing.
- Ability to reason through inconsistent third-party data and design robust normalization logic.
- Comfortable reading external technical documentation and translating it into working code.
- Strong debugging and problem-solving skills.
- Good testing habits using tools such as pytest.
- Ability to work independently and own complex technical work with minimal supervision.
- Clear communication when documenting assumptions, tradeoffs, and implementation decisions.
Nice to have
- Cybersecurity, IT, cloud, identity, or infrastructure data experience.
- Experience with security tools, compliance data, vulnerability data, endpoint data, or SaaS administration data.
- Experience with data contracts, schema evolution, lineage, or data observability.
- Experience building internal developer or data-review tools.
- Familiarity with security/compliance concepts such as risk scoring, controls, frameworks, or remediation workflows.
Full description
Job Description
Senior Python Data Engineer / Applied AI EngineerAbout The Role
- Join a cybersecurity company building AI-enabled data products.
- Work on data integrations, normalization, data quality, and applied AI features.
- Help turn complex security and IT data into reliable, useful product intelligence.
- Own technical problems end-to-end, from investigation through production-ready implementation.
What You’ll Do
- Build and improve Python-based data processing pipelines.
- Work with structured and semi-structured data, especially JSON from third-party APIs.
- Improve data quality, consistency, deduplication, and traceability across integrations.
- Investigate third-party API documentation and identify better ways to collect and use available data.
- Design and implement new integration logic for security, identity, cloud, SaaS, endpoint, and infrastructure data sources.
- Write clear, maintainable Python code for production data workflows.
- Create tests and validation checks to catch schema changes, missing data, malformed records, and edge cases.
- Work with SQL and PostgreSQL to analyze, debug, and improve data flows.
- Contribute to internal tooling that helps the team build, review, and maintain integrations faster.
- Support applied AI/ML features related to classification, enrichment, ranking, entity matching, summarization, or data analysis.
- Collaborate closely with product and engineering to turn messy real-world data into reliable product capabilities.
What We’re Looking ForRequired Qualifications
- A Bachelor’s degree in Engineering is required, though a Master’s degree is preferred.
- 5 + years of relevant professional experience in data engineering.
- Strong software engineering fundamentals: clean code, testing, debugging, version control, and maintainable production systems.
- Engineering, quantitative, or technical background with strong professional software development experience.
- Strong experience working with APIs, JSON, data transformation, and backend data pipelines.
- Strong Python experience, with broader programming experience in other languages welcome.
- Solid SQL skills, ideally with PostgreSQL.
- Experience with ETL/ELT workflows and production data processing.
- Ability to reason through inconsistent third-party data and design robust normalization logic.