Cloud Data Engineer (SQL, AI Search, and Observability)
Date: 10.2025
Function: Engineering
Reports to: Director, Platform Engineering
Location: Remote, United States
Position Summary
As a Cloud Data Engineer, you will be at the intersection of traditional database engineering, modern AI search, and advanced observability practices. This role is ideal for someone who thrives in hybrid environments—combining deep SQL expertise with modern search platforms like Azure AI Search and ElasticSearch. OnBoard is looking for someone who is excited about building intelligent, observable data systems for LLM-powered applications.
You’ll work across structured data platforms, real-time indexing pipelines, and AI-enhanced retrieval systems, ensuring reliability, performance, discoverability, and security. You’ll also contribute to the infrastructure that supports observability for search relevance, query performance, and LLM pipeline diagnostics.
Key Responsibilities
Design, deploy, and operate Microsoft SQL Server workloads across Azure IaaS (VM) and PaaS (Azure SQL DB / SQL Managed Instance).
Build, scale, and tune AI Search and ElasticSearch clusters, pipelines, and relevance models for both keyword and semantic search.
Collaborate with developers and ML teams to build retrieval pipelines for LLM applications, including vector indexing, prompt metadata enrichment, and embeddings.
Integrate observability into all layers of the data/search stack.
Analyze and optimize SQL queries and indexing strategies using Query Store, DMVs, and execution plans.
Secure data access using encryption (TDE, Always Encrypted), private endpoints, and RBAC.
Automate recurring operations using PowerShell/Azure DevOps Pipelines, including job scheduling, alerting, index maintenance and schema validation.
Implement Infrastructure-as-Code (IaC) for SQL and search platforms using Bicep.
Drive incident resolution and root cause analysis for issues across SQL, search, and LLM pipelines.
Maintain technical documentation and operational runbooks for the combined SQL + Search + AI infrastructure.
Skills and Experience Needed
3–5 years of experience managing Microsoft SQL Server in production, both IaaS and PaaS.
Strong experience with full-text and semantic search using Azure Cognitive Search and/or ElasticSearch.
Experience building or maintaining RAG/LLM architectures, including vector stores, embedding generation, and retrieval APIs.
Proficiency in tuning and debugging database performance (wait stats, execution plans, indexing).
Hands-on experience with observability platforms and standards (OpenTelemetry, Grafana, APM tools, etc.).
Automation scripting with PowerShell or Python.
Proficiency with Infrastructure-as-Code (Bicep, Terraform, ARM templates).
Familiarity with compliance, RBAC, and secure data practices in cloud environments.
Bonus: experience instrumenting and debugging LLM inference chains or vector database retrieval quality.
Competencies
Accountability
Adaptability
Technical Curiosity
Business Acumen
Cross-Team Collaboration
Problem Solving
Driving for Results
Creating an Inclusive Environment
Technical/Professional Expertise
Cloud Data Engineer (SQL, AI Search, and Observability)
Date: 10.2025
Function: Engineering
Reports to: Director, Platform Engineering
Location: Remote, United States
Position Summary
As a Cloud Data Engineer, you will be at the intersection of traditional database engineering, modern AI search, and advanced observability practices. This role is ideal for someone who thrives in hybrid environments—combining deep SQL expertise with modern search platforms like Azure AI Search and ElasticSearch. OnBoard is looking for someone who is excited about building intelligent, observable data systems for LLM-powered applications.
You’ll work across structured data platforms, real-time indexing pipelines, and AI-enhanced retrieval systems, ensuring reliability, performance, discoverability, and security. You’ll also contribute to the infrastructure that supports observability for search relevance, query performance, and LLM pipeline diagnostics.
Key Responsibilities
Design, deploy, and operate Microsoft SQL Server workloads across Azure IaaS (VM) and PaaS (Azure SQL DB / SQL Managed Instance).
Build, scale, and tune AI Search and ElasticSearch clusters, pipelines, and relevance models for both keyword and semantic search.
Collaborate with developers and ML teams to build retrieval pipelines for LLM applications, including vector indexing, prompt metadata enrichment, and embeddings.
Integrate observability into all layers of the data/search stack.
Analyze and optimize SQL queries and indexing strategies using Query Store, DMVs, and execution plans.
Secure data access using encryption (TDE, Always Encrypted), private endpoints, and RBAC.
Automate recurring operations using PowerShell/Azure DevOps Pipelines, including job scheduling, alerting, index maintenance and schema validation.
Implement Infrastructure-as-Code (IaC) for SQL and search platforms using Bicep.
Drive incident resolution and root cause analysis for issues across SQL, search, and LLM pipelines.
Maintain technical documentation and operational runbooks for the combined SQL + Search + AI infrastructure.
Skills and Experience Needed
3–5 years of experience managing Microsoft SQL Server in production, both IaaS and PaaS.
Strong experience with full-text and semantic search using Azure Cognitive Search and/or ElasticSearch.
Experience building or maintaining RAG/LLM architectures, including vector stores, embedding generation, and retrieval APIs.
Proficiency in tuning and debugging database performance (wait stats, execution plans, indexing).
Hands-on experience with observability platforms and standards (OpenTelemetry, Grafana, APM tools, etc.).
Automation scripting with PowerShell or Python.
Proficiency with Infrastructure-as-Code (Bicep, Terraform, ARM templates).
Familiarity with compliance, RBAC, and secure data practices in cloud environments.
Bonus: experience instrumenting and debugging LLM inference chains or vector database retrieval quality.
Competencies
Accountability
Adaptability
Technical Curiosity
Business Acumen
Cross-Team Collaboration
Problem Solving
Driving for Results
Creating an Inclusive Environment
Technical/Professional Expertise