Database Administrator

Position Summary

We are seeking a Senior Database Administrator (DBA) with deep, hands-on expertise across PostgreSQL, MongoDB, and Databricks to own the health, performance, scalability, and reliability of our large-scale data environments. This is a highly technical role for someone who thrives on operating databases at very high data volumes and who takes pride in building systems that are fast, resilient, and cost-efficient.

The primary focus of this role is administration and platform engineering: configuring, setting up, and continuously optimizing database platforms; instrumenting comprehensive monitoring and metrics; right-sizing environments in response to changing demand; and standing up schemas and configurations aligned to evolving requirements. As a secondary but important focus, the role supports data analysis activities, troubleshoots production data issues, plans capacity, performs maintenance, and takes proactive action to prevent outages before they occur.

The ideal candidate is technically outstanding across all three platforms, highly collaborative, and genuinely eager to learn and grow as our data estate and technology stack evolve.

Primary Responsibilities — Database Administration & Platform Engineering

Configuration, Setup & Optimization

• Provision and configure PostgreSQL, MongoDB, and Databricks environments from the ground up, applying tuned parameters, storage layouts, and connection strategies appropriate to very large data volumes.
• Optimize database configuration on an ongoing basis — memory, buffers, connection pooling, indexing strategy, partitioning/sharding, vacuum/compaction, query planning, and workload isolation — to sustain performance as volumes grow.
• Stand up schemas and configurations based on functional and non-functional requirements gathered from engineering, analytics, and product stakeholders, balancing normalization, access patterns, and performance.
• Manage Databricks clusters, workspaces, and compute policies, tuning Spark configurations, autoscaling behavior, and storage (Delta Lake) layouts for efficient processing of very large datasets.

Monitoring, Metrics & Observability

• Design and implement monitoring across all three platforms — throughput, latency, query performance, replication lag, lock contention, storage utilization, and resource saturation.
• Establish meaningful metrics, dashboards, and alerting thresholds that surface issues early and drive proactive intervention rather than reactive firefighting.
• Define and track service-level objectives (SLOs) and key operational indicators, and report on the health and performance of the data estate.

Elastic Scaling & Capacity Management

• Resize and scale environments — vertically and horizontally — based on real-time and forecasted demand, ensuring performance during peaks while controlling cost during troughs.
• Implement and tune autoscaling, sharding, replication, and partitioning strategies to accommodate sustained growth in data volume and concurrency.
• Continuously evaluate cost-to-performance trade-offs across cloud storage and compute tiers.

Secondary Responsibilities — Operations, Reliability & Analysis Support

Production Troubleshooting & Proactive Reliability

• Troubleshoot and resolve production data issues, including slow queries, data anomalies, replication problems, contention, and platform incidents — often under time pressure.
• Monitor for performance degradation and take proactive action to prevent outages before they impact users, based on data-driven signals and early warning indicators.
• Participate in incident response and on-call rotations, conduct root-cause analysis, and drive durable fixes and preventative measures.

Capacity Planning & Maintenance

• Plan for capacity upgrades, forecasting growth in storage, compute, and I/O, and scheduling upgrades ahead of demand.
• Perform routine and preventative maintenance — patching, version upgrades, index maintenance, vacuum/compaction, backup verification, and configuration hygiene — with minimal disruption.
• Maintain runbooks, operational documentation, and disaster-recovery procedures.

Data Analysis Support

• Support data analysis activities by assisting analysts and engineers with query optimization, data access, and structuring data for efficient analytical workloads.
• Partner with analytics teams to ensure Databricks and downstream platforms deliver reliable, performant access to large datasets.

Architecture & Design

A defining requirement of this role is the ability to architect data platforms that remain high-performing, highly available, and recoverable at very high data volumes.

• Architect high-performance database designs capable of sustaining very high transaction and query volumes without degradation.
• Design high-availability (HA) architectures — replication, clustering, failover, and multi-zone/multi-region topologies — to meet demanding uptime targets.
• Design and validate disaster-recovery (DR) strategies, including backup/restore, replication, failover runbooks, and defined RPO/RTO targets, with regular DR testing.
• Translate business and technical requirements into scalable, resilient, and cost-effective platform designs across PostgreSQL, MongoDB, and Databricks.