Senior Python Data Engineer / Applied AI Engineer

Job Description

Senior Python Data Engineer / Applied AI Engineer About 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 For

• Required 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.
• 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.
• Relevant Tools & Technologies Python PostgreSQL / SQL dbt or similar data transformation tooling REST APIs JSON schema validation or data-quality frameworks pytest Git Cloud data services, especially Azure or similar platforms Observability/logging tools Applied AI / ML Experience Practical experience using LLM APIs for extraction, classification, enrichment, or internal tooling.
• Practical experience with classical machine learning techniques for classification, regression, clustering, ranking, anomaly detection, or entity matching.
• Experience with embeddings, semantic search, retrieval, or ranking systems.
• Familiarity with libraries such as scikit-learn, sentence-transformers, FAISS, BM25, LightGBM, PyTorch, or similar.
• Ability to use AI where it adds leverage while keeping core data logic reliable, testable, and explainable.
• 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.