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Postgresql Database Engineer Jobs in Charlotte, NC

ML-Ops / Platform Engineer

Newell, NC · On-site

$47.50 - $65.25/hr

... PostgreSQL, Redis, and vector databases, implementing model evaluation, prompt engineering, state management, caching, and high-throughput inference capabilities. · Monitored, troubleshot, and ...

ML-Ops / Platform Engineer

Belmont, NC · On-site

$48.50 - $66.25/hr

... PostgreSQL, Redis, and vector databases, implementing model evaluation, prompt engineering, state management, caching, and high-throughput inference capabilities. · Monitored, troubleshot, and ...

Experience with MongoDB, Redis, PostgreSQL, Vector Databases, caching strategies, state management ... E practices, and production support. * Experience troubleshooting and optimizing cloud-hosted ...

Showing results 41-60

Postgresql Database Engineer information

See Charlotte, NC salary details

$59.1K

$119.3K

$163.6K

How much do postgresql database engineer jobs pay per year?

As of Sep 4, 2026, the average yearly pay for postgresql database engineer in Charlotte, NC is $119,268.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,200.00 and $136,700.00 per year, depending on experience, location, and employer.

What is a PostgreSQL database engineer?

A PostgreSQL Database Engineer is a technology professional who specializes in designing, implementing, maintaining, and optimizing databases using the PostgreSQL relational database management system. Their responsibilities include database schema design, performance tuning, security management, data backup and recovery, and ensuring high availability. They work closely with developers and system administrators to support application requirements and maintain data integrity. PostgreSQL Database Engineers play a critical role in organizations that rely on robust data storage and management solutions.

What are some common challenges faced by PostgreSQL database engineers when optimizing database performance?

PostgreSQL Database Engineers often encounter challenges such as managing query performance bottlenecks, optimizing indexing strategies, and balancing workloads across multiple databases. Large datasets and complex queries can sometimes result in slow response times, requiring engineers to analyze query plans and adjust configurations accordingly. Additionally, ensuring data consistency and high availability in distributed environments can be demanding, especially when working with replication and backup strategies. Collaboration with developers and system administrators is crucial to address these challenges effectively and maintain optimal database performance.

What are the key skills and qualifications needed to thrive as a PostgreSQL database engineer, and why are they important?

To thrive as a PostgreSQL Database Engineer, you need strong expertise in SQL, database design, performance tuning, and a solid understanding of PostgreSQL architecture, often supported by a degree in computer science or a related field. Familiarity with tools like pgAdmin, backup and replication utilities, and relevant certifications such as the EDB PostgreSQL Associate are highly valuable. Attention to detail, problem-solving abilities, and effective communication skills help you excel in managing data integrity and collaborating with development teams. These competencies are essential to ensure reliable, optimized, and secure database environments that support critical business operations.

What is the difference between Postgresql Database Engineer vs Database Administrator?

AspectPostgresql Database EngineerDatabase Administrator
Primary FocusDesigning, developing, and optimizing PostgreSQL databasesMaintaining, securing, and ensuring the performance of databases
Skills & CertificationsSQL, PostgreSQL expertise, scripting, performance tuningDatabase management, backup/recovery, security protocols
Work EnvironmentDevelopment teams, data engineering projectsIT operations, support teams, enterprise environments

While both roles involve working with databases, a PostgreSQL Database Engineer primarily focuses on designing and optimizing PostgreSQL databases, whereas a Database Administrator handles ongoing maintenance, security, and performance management. The roles often overlap but serve different stages of the database lifecycle.

What are popular job titles related to Postgresql Database Engineer jobs in Charlotte, NC?

For Postgresql Database Engineer jobs in Charlotte, NC, the most frequently searched job titles are:

What job categories do people searching Postgresql Database Engineer jobs in Charlotte, NC look for?

The top searched job categories for Postgresql Database Engineer jobs in Charlotte, NC are:

ML-Ops / Platform Engineer

Long Finch Technologies

Mount Holly, NC • On-site

$48.75 - $66.75/hr

Full-time

Posted 16 days ago


Job description

Must have skills: MLOps, AWS/Azure, Kubernetes, Docker, Python, Terraform, CI/CD, GenAI/LLM, RAG, Bedrock/Azure OpenAI, Vector DB, Observability Responsibilities:

·       Designed, deployed, and operated enterprise-grade MLOps/GenAI platforms across AWS and Azure, leveraging AWS Bedrock, SageMaker, Azure OpenAI, Azure AI Foundry, model serving, embeddings, RAG, vector databases, AI gateways, guardrails, and agentic frameworks.

·       Built and automated cloud-native ML infrastructure using Terraform, Kubernetes (EKS/AKS/OpenShift), Docker, GitHub Actions, Azure DevOps, Jenkins, GitOps, and ArgoCD, enabling scalable model deployment, CI/CD, versioning, and release management.

·       Implemented secure and highly available ML/AI platforms using AWS IAM, Azure IAM/RBAC, VPC/VNet, Key Vault, Secrets Manager, API Gateway, load balancers, ingress controllers, service mesh, autoscaling, and multi-account/subscription architectures.

·       Developed and operationalized ML/GenAI workloads using Python, REST APIs, microservices, MongoDB, PostgreSQL, Redis, and vector databases, implementing model evaluation, prompt engineering, state management, caching, and high-throughput inference capabilities.

·       Monitored, troubleshot, and optimized production ML/AI workloads using observability, logging, monitoring, SRE practices, performance tuning, resiliency, disaster recovery, and cost optimization, while collaborating with application, platform, infrastructure, and security teams.