Senior Database Administrator
The Senior Database Administrator will administer AWS-hosted PostgreSQL databases, lead query and schema optimization, guide data architecture, and mentor engineers. The role requires 7+ years managing enterprise databases, strong SQL expertise, and experience with cloud database services and AI-assisted technical workflows.
About the job
Responsibilities
- Administer and monitor AWS PostgreSQL databases, including RDS and Aurora, for reliability, performance, and day-to-day operations.
- Lead database performance optimization, identify bottlenecks, and implement advanced query-tuning solutions.
- Review and approve developer-driven data architecture changes; establish database guidelines and provide structured feedback.
- Assess schemas, indexes, columns, and table structures for efficiency and scalability.
- Participate in release and deployment processes and support production workflows.
- Automate manual tasks, improve data delivery, and support infrastructure growth.
- Use AI agents, LLM-powered tools, and automation for query analysis, schema reviews, documentation, and troubleshooting.
- Research technologies and propose AI-driven productivity and infrastructure improvements.
- Mentor junior and mid-level engineers and promote technical best practices and knowledge sharing.
- Collaborate with engineering teams, leadership, and other departments.
Requirements
- Experience designing, building, and scaling relational databases and data architectures through professional work, academic projects, internships, or personal exploration.
- Familiarity with SQL and interest in PostgreSQL and cloud database services.
- Comfort using AI assistants, copilots, and LLM-powered tools as part of daily technical workflows.
- 7–8 years of professional experience as a Database Administrator or Database Engineer managing mission-critical databases.
- 7–8 years of experience with enterprise databases and scalable data architectures.
- Technical mentorship, team leadership, and cross-functional knowledge-sharing experience.
Nice-to-Haves
- Hands-on experience with AWS RDS or Aurora.
- Experience using AI agents or LLM-powered tools for technical work.
- PostgreSQL and Microsoft SQL Server.
- Python and shell scripting.
- Snowflake, Fivetran, Celigo, or Boomi.
- GitHub Actions, Terraform, Liquibase, or Jenkins.
- Datadog, Power BI, Jira, or Slack.
Skills
Postgres, Aws Rds, Amazon Aurora, SQL, Python, Shell Scripting, Terraform, Liquibase, Jenkins, Snowflake, Microsoft Sql Server, GitHub Actions, Datadog, Power BI, Llm Tools
Similar jobs
Data Engineering jobs
Addepar
United States
Staff Software Engineer building scalable frameworks for high-performance financial data ingestion/distribution and AI-native products. Requires 7+ years experience with distributed systems, microservices, and data architectures; partners with product teams to drive technical direction.
Alpaca
Remote
Build and operate scalable lakehouse infrastructure, streaming and CDC pipelines, query systems, and self-serve BI capabilities. Requires 5+ years of data engineering experience, strong Kubernetes and infrastructure-as-code expertise, and hands-on experience with distributed data platforms.
NinjaTrader
Chicago, IL
Leads database architecture, performance, reliability, and developer-tooling initiatives for high-volume trading applications. Requires 8+ years of software engineering experience, expert MySQL skills, backend development expertise, and strong knowledge of distributed systems and database operations.
Deepgram
United States
Own the company’s metric governance program by defining canonical metrics, enforcing them in semantic and catalog systems, improving data quality, and validating AI-agent outputs. Requires 5+ years in analytics or analytics engineering, strong SQL, production semantic-layer ownership, and experience with AI evaluation and data governance.
Aleph
United States
Own and scale transformation pipelines that convert diverse financial and operational data into reliable FP&A-ready models. The role requires strong SQL and dbt expertise, data integrity and performance skills, and effective collaboration across Engineering and Customer Success.