About the role
Role SummaryWe are seeking a Senior Database Administrator (DBA) with deep, hands-on expertise in PostgreSQL and in analytical (OLAP) and data warehouse platforms such as StarRocks, ClickHouse, or similar. You will own the health, performance, scalability, and reliability of the data stores that power real-time analytics and customer-facing reporting at very high data volumes. You take pride in building systems that are fast, resilient, and cost-efficient.
The primary focus is administration and platform engineering: configuring and tuning database platforms, instrumenting monitoring and metrics, right-sizing environments as demand changes, and standing up schemas and data models aligned to evolving requirements. Secondary focus: supporting analytics teams, troubleshooting production data issues, planning capacity, performing maintenance, and preventing outages before they happen.The ideal candidate is technically outstanding across both transactional and analytical systems, highly collaborative, and eager to keep learning as our data estate evolves.
Primary Responsibilities: Administration & Platform Engineering Configuration, Setup & Optimization
- Provision and configure PostgreSQL and OLAP warehouse clusters (StarRocks, ClickHouse, Doris, Redshift, BigQuery, or similar) from the ground up, with tuned parameters, storage layouts, and connection strategies suited to very large data volumes.
- Continuously optimize PostgreSQL: memory and buffers, connection pooling (PgBouncer), indexing, partitioning, vacuum/autovacuum, query planning, and workload isolation.
- Continuously optimize OLAP and warehouse engines (concepts apply across tools): table models and keys (duplicate, aggregate, primary key), partitioning and bucketing/distribution, sort keys, materialized views, compaction, caching, and resource groups for workload isolation.
- Design and tune ingestion into the warehouse: streaming and batch loads (Kafka, Flink CDC, Stream Load/Routine Load, S3 or object-storage loads), handling late data, upserts, and schema evolution.
- Stand up schemas and data models from functional and non-functional requirements gathered from engineering, analytics, and product stakeholders, balancing normalization, denormalization, access patterns, and performance.
- Tune query performance using query profiles, EXPLAIN plans, join strategies, and data skew analysis to keep dashboards and ad hoc queries fast at scale.
- Design and implement monitoring across all platforms: throughput, latency, query performance, replication lag, ingestion lag, lock contention, compaction backlog, storage utilization, and resource saturation.
- Build dashboards and alerting (e.g., Prometheus, Grafana) with 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 clusters vertically and horizontally (adding or removing frontend/backend or shard/replica nodes, rebalancing tablets or partitions) based on real-time and forecasted demand.
- Tune replication, sharding, and partitioning strategies to support sustained growth in data volume and query concurrency.
- Continuously evaluate cost-to-performance trade-offs across storage and compute tiers, including hot/cold storage, tiered storage, and compute/storage separation.
- Troubleshoot and resolve production data issues, including slow queries, data anomalies, replication or ingestion problems, contention, and platform incidents, often under time pressure.
- Watch for performance degradation and act before it affects users, using data-driven signals and early-warning indicators.
- Participate in incident response and on-call rotations, run root-cause analysis, and drive durable fixes.
- Forecast growth in storage, compute, and I/O, and schedule upgrades ahead of demand.
- Perform routine and preventative maintenance (patching, version upgrades, index and partition maintenance, vacuum/compaction, backup verification, configuration hygiene) with minimal disruption.
- Maintain runbooks, operational documentation, and disaster-recovery procedures.
- Help analysts and engineers with query optimization, data access, and data modeling for efficient analytical workloads.
- Partner with analytics and BI teams (e.g., Superset, Power BI) to ensure dashboards and downstream tools get reliable, fast access to large datasets.
- A defining requirement of this role is the ability to architect data platforms that stay high performing, highly available, and recoverable at very high data volumes.
- Architect transactional (PostgreSQL) and analytical (OLAP) designs that sustain very high write, ingestion, and query volumes without degradation.
- Design the data flow between systems, such as operational PostgreSQL to the warehouse via CDC, and choose the right engine for each workload (OLTP, real-time OLAP, batch analytics).
- Design high-availability (HA) architectures: replication, clustering, failover, and multi zone or 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.
- Extensive hands-on database administration experience, with senior-level depth in PostgreSQL.
- Hands-on production experience with at least one modern OLAP/columnar warehouse engine (for example StarRocks, ClickHouse, Apache Doris, Druid, Redshift, or BigQuery; depth in the underlying concepts matters more than the specific tool), including data modeling, partitioning, and performance tuning.
- Demonstrated experience scaling data environments for very large data volumes, including sharding, partitioning, replication, and tuning at scale.
- Deep expertise in configuration, optimization, indexing, and query performance tuning for both row-store and columnar systems.
- Proven experience designing and operating HA and DR solutions for high-volume databases.
- Strong monitoring and observability skills: instrumenting metrics, dashboards, and alerting to drive proactive operations.
- Experience with capacity planning, maintenance, patching, and upgrade management in production.
- Solid command of SQL and a scripting/automation language (e.g., Python, Bash) for operational tooling
- Working knowledge of data pipelines and CDC/streaming tools (e.g., Kafka, Flink, Debezium) and of cloud storage and compute scaling.
- Strong troubleshooting and root-cause analysis skills, with the ability to stay calm and effective during production incidents.
- Experience with infrastructure-as-code and automation (e.g., Terraform, Ansible) and CI/CD for database changes.
- Familiarity with Docker and Kubernetes for running database and data workloads (e.g., operators for PostgreSQL, StarRocks, or ClickHouse).
- Experience with data lake and lakehouse formats (Parquet, Iceberg, Delta) and external or federated querying from the warehouse.
- Exposure to BI and visualization tools (Superset, Power BI, Grafana) and how their query patterns affect warehouse design.
- Exposure to data governance, security, encryption, role-based access, and compliance requirements at scale.
- Relevant certifications in PostgreSQL, a data warehouse or OLAP platform, or major cloud providers.
- Prior experience mentoring engineers or leading platform initiatives.
- Technically savvy: genuinely strong and current in PostgreSQL and OLAP warehouse engines, with the instincts to diagnose and optimize complex systems.
- Highly collaborative: works closely with engineering, analytics, and product teams and communicates clearly with technical and non-technical audiences.
- Eager to learn and grow: curious, adaptable, and motivated to expand skills as 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.
Skan AI is an equal opportunity employer committed to building a diverse, inclusive, and respectful workplace around the world. We do not discriminate based on race, color, religion or belief, sex (including pregnancy, sexual orientation, gender identity, or gender expression), national origin, ancestry, age, disability, medical condition, genetic information, marital or family status, military or veteran status, or any other characteristic protected by applicable laws in the locations where we operate.We welcome people from all backgrounds and provide reasonable accommodations throughout the hiring process.