Senior Python Data Engineer​/Applied AI Engineer

Position: Senior Python Data Engineer / Applied AI Engineer
Location: Northern

Senior Python Data Engineer / Applied AI Engineer Job details

Product

United States Full-time

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.
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