Note: The job is a remote job and is open to candidates in USA. Skan AI is seeking a Senior Database Administrator to own the health, performance, scalability, and reliability of its PostgreSQL, MongoDB, and Databricks environments. The role focuses on database platform engineering, monitoring, scaling, architecture, production reliability, and supporting data analysis.
Responsibilities
- 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
- 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
- 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
- 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
- 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
- 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
- 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
Skills
- Extensive hands-on database administration experience, with **senior-level depth in PostgreSQL, MongoDB, and Databricks**
- Demonstrated experience **scaling data environments for very large data volumes**, including sharding, partitioning, replication, and performance tuning at scale
- Deep expertise in **database configuration, optimization, indexing, and query performance tuning**
- Proven experience designing and operating **high-availability and disaster-recovery** solutions for high-volume databases
- Strong skills in **monitoring and observability** — instrumenting metrics, dashboards, and alerting to drive proactive operations
- Experience with **capacity planning, maintenance, patching, and upgrade management** in production environments
- Solid command of **SQL** and a **scripting/automation language** (e.g., Python, Bash) for operational tooling and automation
- Working knowledge of **cloud data platforms**, storage/compute scaling, and Delta Lake / Spark concepts within Databricks
- Strong troubleshooting and root-cause analysis skills, with the ability to stay calm and effective during production incidents
- **Technically savvy** — genuinely strong and current across all three platforms, with the instincts to diagnose and optimize complex systems
- **Highly collaborative** — works closely with engineering, analytics, and product teams and communicates clearly with both technical and non-technical audiences
- **Eager to learn and grow** — curious, adaptable, and motivated to expand skills as the data estate and technologies evolve
- **Proactive and ownership-minded** — anticipates problems, prevents outages, and takes end-to-end responsibility for platform health
- **Detail-oriented and reliable** — disciplined about maintenance, documentation, and operational rigor
- Experience with infrastructure-as-code and automation (e.g., Terraform, Ansible) and CI/CD for database changes
- Familiarity with containerization and orchestration (Docker, Kubernetes) for database and data workloads
- Exposure to data governance, security, encryption, and compliance requirements at scale
- Relevant certifications across PostgreSQL, MongoDB, Databricks, or major cloud providers
- Prior experience mentoring engineers or leading platform initiatives
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