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

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.