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Lunar Energy

Data Infrastructure Engineer (contract)

Mountain View, CA$130.9k–$141.4kcontractmidAdded today

About this role

Lunar Energy seeks a Data Infrastructure Engineer for a 6-month contract to design, deploy, and maintain a hybrid data platform spanning Vertica, SQL Server, PostgreSQL, and virtualized infrastructure. You'll manage multi-tier database clusters, automate operations via PowerShell and Python, optimize ETL pipelines, and ensure reliability and performance across production and development environments.

What you'll do

  • Design and maintain Vertica analytics clusters including topology, K-safety, projections, and query tuning
  • Administer SQL Server 2016/2019 with Always On Availability Groups on virtualized Windows infrastructure
  • Write PowerShell and Python scripts to automate deployments, patching, monitoring, and operational tooling
  • Build and optimize ETL pipelines across Vertica, SQL Server, PostgreSQL, and file sources with data quality checks
  • Manage PostgreSQL instances on Linux including roles, schemas, backups, and recovery integration
  • Oversee hypervisor platform (VMware vSphere/ESXi), VM provisioning, tuning, and incident response

What they're looking for

  • Vertica database administration and cluster management
  • SQL Server 2016/2019 and Always On Availability Groups
  • PostgreSQL administration on Linux (RHEL, AlmaLinux, Ubuntu LTS)
  • PowerShell and Python scripting for automation and ETL
  • ETL pipeline design with error handling and scheduling
  • VMware vSphere/ESXi hypervisor management
  • Windows Server 2019 and Linux system administration
  • NAS/SAN storage and IOPS planning
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Lunar Energy

Lunar Energy builds hardware and software systems that require sophisticated integration, testing, and field deployment. The company is hiring Systems Test Engineers, Field Service Engineers, and Data Infrastructure Engineers to support product launches, system integration, and enterprise data platform operations.

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Likely interview questions

  • Walk us through your experience administering Vertica or similar columnar analytics databases—how have you optimized cluster performance and projection design?
  • Describe a complex ETL pipeline you've built; how did you handle error handling, retry logic, and data quality validation across heterogeneous sources?
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