Core Focus Areas
This is a wide-scope role. Here is how candidates should prioritize their self-evaluation:
01 Oracle + Distributed Systems Technical Depth — Deep in one, strong across the other — enough to lead architecture and hold the team accountable
02 Data Platform Reliability & Incident Ownership — SLOs, escalation authority, DR, predictive capacity — the foundational mandate
03 AI-Driven Automation & Toil Elimination — Building and shipping agents, not just evaluating tools — measurable impact on MTTR and operational load
04 Team Leadership & Org Development — Managing ~30 across ICs and managers; building culture, career ladders, and hiring bar
05 Vendor Management & Cost Optimization — Oracle relationship, licensing discipline, and infrastructure cost efficiency
What Makes This Role Unique
→ Breadth of scope, depth of ownership — You hold the charter for Oracle/Exadata AND the full open-source big data stack — accountable for reliability across all of it
→ Build AI systems, not just use them — We expect shipped production tooling — AI agents, autonomous remediation, intelligent observability — not proof-of-concept demos
→ Strategy and execution in equal measure — You will define the multi-year data platform roadmap and then make it happen — this is not a pure operations role
→ Real cross-functional influence — Data reliability touches every product team; this role earns authority through technical credibility, not org chart position
→ A platform worth modernizing — Battle-tested at scale, with genuine complexity — and a leadership team committed to doing it right
What You Will Build
Beyond running the platform, we expect this leader to leave a concrete engineering footprint. Examples of the systems we want this role to build and own:
AI-assisted incident triage system — LLM-powered log analysis and alert summarization that cuts triage time from minutes to seconds
Autonomous remediation agents — Self-healing workflows for common failure patterns — space exhaustion, replication lag, lock contention, consumer group lag — with safe human escalation fallback
Natural-language operational query interface — Engineers ask questions in plain English; the system queries metrics, logs, and configuration state to answer
Predictive capacity and anomaly models — ML-driven forecasting for Oracle tablespace growth, Kafka partition pressure, and Cassandra compaction backlog
Reliability-as-code pipeline gates — Automated schema review, load test, and regression detection integrated into CI/CD so reliability issues are caught before production
What You Will Do
Own the Data Platform Reliability Charter
Define and drive SLOs, SLIs, and error budgets across Oracle, Exadata, Kafka, Cassandra, Hadoop, OpenSearch, Redis, Ceph, and Kubernetes-hosted data services
Serve as the final technical escalation point for all P0/P1 incidents; lead blameless post-incident reviews and drive permanent resolution — not workarounds
Own disaster recovery architecture, backup integrity standards, and data durability SLAs — validated through regular DR exercises
Establish predictive capacity planning and performance baselines across the full data platform footprint
Drive cost efficiency and infrastructure utilization optimization — right-sizing resources, eliminating idle capacity, and building chargeback/showback visibility for leadership
Define and Execute the Multi-Year Data Platform Strategy
Develop and own a multi-year DBRE roadmap — covering reliability targets, modernization priorities, platform consolidation opportunities, and AI automation milestones
Drive platform evolution decisions: when to standardize, when to consolidate, when to retire, and when to invest in new capabilities
Provide architectural guidance for major data platform initiatives — schema design, topology changes, new technology adoption, and migration planning
Partner with Product Engineering, Security, and Infrastructure leadership to embed reliability and cost constraints into platform direction from day one
Go Deep on Oracle and Exadata
Carry deep technical authority for Oracle Database (19c+), RAC, Data Guard, RMAN, and Exadata — sufficient to lead architecture reviews, diagnose production incidents, and hold senior engineers accountable
Drive query optimization, AWR/ASH-based diagnostics, execution plan analysis, and schema design reviews for mission-critical OLTP and analytical workloads
Manage Oracle vendor relationships with technical and commercial depth: negotiate support contracts, challenge licensing interpretations, escalate through Oracle's senior channels, and ensure Qualys negotiates from a position of knowledge
Own Oracle licensing compliance end-to-end — a material financial and risk area requiring both engineering precision and commercial acumen
Lead Exadata patching cadence, storage cell failure playbooks, IORM policy tuning, and hardware lifecycle planning
Lead the Open-Source Big Data Platform
Architect and operate Kafka, Cassandra, Hadoop, OpenSearch, Redis, and Ceph deployments — self-managed at scale, not cloud-managed services
Apply strong distributed systems fundamentals — consensus and replication models, storage I/O patterns, network latency sensitivity, and horizontal scaling — to architectural decisions that hold under load
Partner with DevOps and Platform Engineering on Kubernetes-native stateful workloads: operators, persistent volumes, storage class optimization, and capacity planning
Drive standardization of runbooks, alerting, and operational playbooks across all technologies — documented, automated, no tribal knowledge
Build AI-Powered Reliability Systems
Design and ship AI agents that automate tier-1 database operational toil — systems running in production with measurable impact on MTTR and alert noise
Integrate LLM APIs (Anthropic, OpenAI, Gemini) into operational workflows for log triage, incident summarization, runbook execution, and anomaly-triggered decision support
Evaluate AIOps platforms and observability tooling with engineering rigor — quantify the actual value, not the vendor pitch
Embed reliability-by-design upstream: schema review gates, load test automation, and database performance regression detection in CI/CD pipelines
Lead and Develop the DBRE Team
Manage, mentor, and develop ~30 DBAs, SREs, and data platform engineers across individual contributors and engineering managers
Define hiring standards, career ladders, and team structure that attract and retain senior database and reliability talent
Build a culture of technical rigor, blameless learning, and relentless automation — where manual toil is a bug, not a process
Eliminate single points of knowledge failure through documentation, cross-training, and disciplined on-call rotation design
Own workforce planning: headcount forecasting, skills gap analysis, and building specialist sub-teams as the platform evolves
How We Measure Success
→ Platform Availability — SLO attainment across all data services — reduction in unplanned production incidents attributable to DBRE gaps
→ MTTA / MTTR — Measurable improvement in time to detect and resolve incidents, accelerated by AI tooling
→ Automation Coverage — Percentage of tier-1 operational tasks eliminated through shipped agents and tooling
→ Cost Efficiency — Infrastructure utilization rates, Oracle licensing optimization, and idle/over-provisioned resource reduction
→ Team Health & Retention — On-call load reduction, career growth trajectory, and senior IC/manager retention
→ Platform Strategy Execution — Multi-year roadmap milestones delivered; capacity forecasts validated against actual growth
What We Are Looking For
Must Have
15+ years in database engineering, reliability engineering, or data platform operations — with 5+ years leading multi-person teams including at least one layer of managers
Deep expertise in Oracle or large-scale distributed data systems, with strong working knowledge across the broader ecosystem (Kafka, Cassandra, Hadoop, OpenSearch, Redis, Ceph, Kubernetes)
Strong distributed systems fundamentals — replication and consensus models, storage I/O, network latency tradeoffs, and scaling patterns for stateful workloads — applied to real production decisions
Shipped operational tooling or agents using LLM APIs with measurable impact (MTTR reduction, toil elimination, alert noise reduction) — production systems, not proofs of concept
Oracle vendor relationship management — commercial negotiation, license compliance ownership, and technical escalation through Oracle's support and sales organizations
Infrastructure cost control — hands-on experience right-sizing compute and storage, eliminating idle capacity, and building cost visibility (chargeback/showback) across database infrastructure
Track record leading teams of 15+ engineers with both IC and manager-level reports; experience growing technical talent and building hiring bars
Executive communication skills — translating database risk, reliability posture, and investment needs to C-suite audiences with clarity and commercial grounding
Experience serving as final escalation point for production data platform incidents — with the technical depth to diagnose, direct, and close
Strong Plus
Exadata-specific depth: IORM policy tuning, smart scan diagnostics, storage server failure recovery, and hardware lifecycle decision-making
Kubernetes-native data platform experience with stateful workload operators (Strimzi, Cassandra Operator, OpenSearch Operator, Redis Operator)
Experience with AIOps or observability platforms (Datadog, Grafana, OpenTelemetry) including custom exporter and agent development
Familiarity with agentic integration patterns (MCP, tool-use APIs) for building AI-augmented operational toolchains
Hands-on coding in Python, Go, or shell scripting — enough to design and ship internal tooling independently
Background in security or compliance-adjacent industries where data integrity and auditability carry regulatory weight
Who This Role Is For
This role is for a data platform leader who still gets pulled in by a gnarly execution plan, and who sees a recurring on-call alert as a signal that something needs to be built — then builds it. You lead by doing as much as by directing, and your team follows you because they know you have sat in their chair.
You hold Oracle's feet to the fire in a negotiation and hold your engineers to a high bar in a design review. You speak reliability to a DBA and business risk to a CFO. You don't wait to be handed a mandate — you see the gap, take the charter, and grow the team around it. You balance operating rigorously today with shaping the platform for where the business will be in three years.
If you are energized by owning something truly foundational — the data layer that everything else at Qualys depends on — and you want to build the next generation of AI-driven reliability engineering, we would like to hear from you.
This Role Is Not For
DBAs focused on ticket-based operations who are not comfortable driving architectural decisions and organizational change
Leaders who are not hands-on in production systems — this role requires genuine technical engagement, not just team management
Candidates whose AI experience is limited to tool adoption — we need operators who have shipped agents and measured the impact
Candidates who need full-stack mastery on day one — we value depth in core areas and intellectual range over checklist coverage
About Qualys
Qualys, Inc. (NASDAQ: QLYS) is a pioneer and leading provider of disruptive cloud-based security, compliance, and IT solutions with more than 10,000 subscription customers worldwide, including a majority of the Forbes Global 100 and Fortune 100. Qualys helps organizations streamline and automate their security and compliance solutions onto a single platform for greater agility, better business outcomes, and substantial cost savings.
Qualys is an Equal Opportunity Employer. Please see our EEO policy for details.
