Be at the Forefront of the Agentic AI Revolution

At Skan AI, you'll be part of the team pioneering the context engine for human and agentic execution, bringing context from enterprise operators, systems, and processes to power how the world's largest organizations execute their most complex, mission-critical work.

We're in hyper-growth mode at exactly the right moment in history. As enterprises race to adopt agentic AI, we're uniquely positioned to deliver the clear signal they desperately need: a platform that trains and grounds AI Agents in trillions of real execution signals, enabling reliable, compliant automation of their most complex processes.

Backed by Dell Technologies Capital and other leading investors, we're the only company that can bridge the gap between AI's promise and enterprise reality, making us perfectly positioned to define the agentic era for modern enterprises.

Our diverse, team-oriented team of 250+ innovators is solving category-defining challenges at the intersection of AI, process intelligence, and enterprise work. Diverse perspectives fuel breakthrough thinking, cross-functional collaboration is the norm, and our work directly transforms how Fortune 500 companies operate. We are shaping the future of work itself.

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 perfo

📌 Senior Database Administrator (Winnipeg)
🏢 SkanAI
📍 Winnipeg