Log Analytics Engineer
OpenKyber LLC
Atlanta, GA, United States
1 day ago
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Role details
Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Experienced
Experience required
3 years minimum
Compensation
$135,200.0 - $156,000.0
Working hours
Regular working hours
Job source
Tech stack
Artificial Intelligence
Data Analysis
Software Code Optimization
Data Architecture
Information Engineering
Data Governance
Log Analysis
Machine Learning
Operational Databases
Reliability Engineering
Search Technologies
Machine Learning Operations
+2 more
Data Pipelines
Databricks
Job description
- Architect & Deliver Data/AI Solutions: Lead the technical vision and end-to-end delivery of analytics, machine learning, predictive modeling, and GenAI use cases on the enterprise Databricks platform.
- Build Scalable Data Pipelines: Design, optimize, and oversee production data pipelines utilizing Databricks Workflows and the Medallion Architecture to ingest and curate complex sensor feeds, inspection records, maintenance histories, and operational/financial datasets.
- Advanced Predictive & Lifecycle Modeling: Guide the design and deployment of predictive health models, failure probability algorithms, and Remaining Useful Life (RUL) indicators, alongside financial lifecycle cost models for risk-based capital allocation.
- Governance & Platform Optimization: Implement enterprise-grade data governance, lineage, and security frameworks using Unity Catalog, while continuously evaluating and integrating modern Databricks features (e.g., Delta Live Tables, MLflow, Vector Search).
- Cross-Functional & Business Stakeholder Alignment: Partner closely with engineering, reliability, and asset management teams to deploy interactive dashboards and risk-scoring frameworks aligned with industry standards (e.g., ISO 55000) and regulatory requirements.
- Workstream Program Leadership: Serve as a core technical anchor within the Infrastructure EAM workstream, bridging executive strategy and technical execution during high-demand project phases to reduce operational risk and project costs.
Requirements
- 7+ years of progressive experience in data architecture, data engineering, or advanced asset analytics roles.
- 3+ years of program or project leadership experience driving complex enterprise data solutions or asset management initiatives.
- Proven Hands-On Databricks Expertise: Strong practical command of the Databricks Lakehouse ecosystem, Medallion Architecture, Unity Catalog, and modern ML/AI tooling.
- Domain Knowledge: Solid understanding of reliability engineering, condition-based monitoring, predictive maintenance techniques, or enterprise asset management (EAM) concepts.
- Preferred Experience: Direct experience working with rail infrastructure, transit networks, or linear assets.
- Preferred Certifications: Databricks Certified Data Engineer Professional or Databricks Certified Machine Learning Professional/Associate.
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