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Senior Python Data Engineer / Applied AI Engineer

Guardare United States

Jun 23
python Senior (5-10 yrs) Full-time United States
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About the role

Build and maintain Python-based data processing pipelines to integrate and normalize complex security and IT data. Collaborate with product and engineering teams to implement applied AI features and ensure high-quality, reliable data intelligence.

What they look for

Python PostgreSQL SQL Data Engineering ETL/ELT REST APIs JSON Applied AI Machine Learning Data Pipelines Pytest Git Data Quality Cloud Services LLM APIs Normalization

Requirements

Requires a Bachelor's degree in Engineering and at least 5 years of professional data engineering experience. Candidates must possess strong Python and SQL skills, along with experience in API integration and production-ready data workflows.

Full description

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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