About HG Insights
Headquartered in Santa Barbara, California, HG Insights is the global leader in technology intelligence. We help the world's most innovative companies accelerate their go-to-market efforts with precision through advanced data science methodologies and proprietary data assets. We offer a culture that blends innovation, collaboration, and growth, where each team member is empowered to make a measurable impact.
Role Overview
HG Insights runs data infrastructure across three organizations, two clouds, and sixteen database and caching technologies. As we scale, we're consolidating database ownership under Platform Engineering. This role leads that function.
You will be the first database specialist on our DevOps team; embedded, not siloed. You'll own the full database portfolio: operational health, security posture, cost optimization, and the migration work required to modernize our data layer. As you build the practice, you'll cross-train DevOps engineers so the capability compounds beyond one person.
This is a Senior/Staff individual contributor role. You will set direction on database strategy and own its execution. The team you're joining manages 140+ IaC repositories across AWS, GCP, and Kubernetes.
Job Overview
Database ownership across the portfolio
- Primary operator for PostgreSQL (RDS, Aurora, Cloud SQL, NeonDB), MySQL, Redshift, ClickHouse, Elasticsearch, OpenSearch, MongoDB Atlas, and Databricks
- Caching layer ownership: ElastiCache Redis, Dragonfly, Valkey
- Version management and patching across a multi-repo Terraform estate; coordinate EOL remediations across AWS and GCP
- Establish backup, recovery, and DR standards across all managed data layers
Migration and modernization
- Lead Elasticsearch modernization; approved architecture, ready to execute
- Lead ClickHouse stabilization and standardization
- Phoenix data repatriation: move third-party-hosted data in-house as Phoenix migration progresses
- NeonDB consolidation evaluation
Performance and cost
- Own database cost optimization across the portfolio: capacity planning, rightsizing, reserved instance and savings plan recommendations
- Query analysis, slow query identification, connection pool tuning
- Identify and execute on addressable savings in the database infrastructure spend
Platform integration
- Terraform and Terraform Cloud for all database infrastructure; standardize configurations across a fragmented estate
- ArgoCD and Kubernetes (EKS, GKE) for database workloads running in-cluster (Elasticsearch, ClickHouse, Dragonfly)
- GitHub Actions CI for database migration testing and backup validation
- Build and own the database observability layer in Datadog: dashboards, monitors, slow query alerting, replication lag, connection saturation
Collaboration with application engineering
- Partner with application engineering teams on database design, schema changes, query optimization, and technology selection; you are the subject matter authority they escalate to
- Establish and maintain standards for how application teams interact with the data layer: migration tooling, connection management, index design review, and capacity requests
- Own the feedback loop between application performance and database infrastructure; connect slow query data to engineering teams who can act on it
Cross-training and knowledge transfer
- Structured cross-training with the DVO team so database operational capability isn't concentrated in one person
- Document operational runbooks, incident response procedures, and capacity planning models
- 5+ years of hands-on database administration or database engineering; not theoretical
- Deep PostgreSQL expertise: replication, vacuum tuning, connection pooling (PgBouncer/PgCat), index strategy, major version upgrades
- Elasticsearch operational experience at scale; index lifecycle management, shard sizing, snapshot/restore, Kubernetes-hosted clusters
- ClickHouse or columnar database experience is a strong plus
- AWS RDS, Aurora, Redshift, ElastiCache; day-to-day operations, not just provisioning
- Terraform and Terraform Cloud; you will be reading and writing IaC on day one
- Kubernetes (EKS or GKE); database workloads run in-cluster and you need to be comfortable there
- Python or Bash scripting; maintenance windows, backup validation, migration tooling
- Migration experience; schema migrations, cross-engine moves, zero-downtime cutover strategies
- Datadog or equivalent observability tooling
Nice to have
- BigQuery, DynamoDB, or Databricks administration
- Redis/Valkey/Dragonfly at production scale
- MongoDB Atlas administration
- Cloud cost optimization (reserved capacity, savings plans)
