The explosion of “Ops” roles — unravelling the common thread in modern engineering

Why every “Ops” role shares a core purpose — and how to find it

Takeaways

• “Ops” roles are proliferating rapidly in engineering, with new variants like DevOps, CloudOps, DataOps, and MLOps emerging every year.
• While the growing number of Ops roles initially seems innovative, it can lead to fragmentation, confusion and unclear ownership within teams.
• Despite their differences, all Ops roles share a common underlying purpose: helping engineering succeed in production reliably, securely and at scale.
• The main challenge is managing the complexity and finding the unifying thread — what truly connects all these roles — to ensure organizational success rather than chaos.
• Ops disciplines approach their shared goal from various domains such as code, infrastructure, data, ML models, security, cost, and platforms, contributing to a diverse operational ecosystem.

If you’ve been around modern engineering organizations, you’ve probably noticed a pattern: Every year, a new “Ops” role emerges. DevOps, DevSecOps, CloudOps, DataOps, MLOps, FinOps, GitOps, PlatformOps, AIOps, ModelOps ... and the list keeps growing.

At first, it feels like innovation. Then it feels like fragmentation. Before long, teams begin speaking different operational dialects, tools multiply, ownership becomes fuzzy, and leaders start asking:

• Are we overcomplicating engineering operations?
• Do we really need this many Ops roles?
• What is the common thread between all of them?

Let’s map the modern Engineering Ops ecosystem, clarify what each role truly means, and then uncover the one common thread that powers all of them — the force that determines whether your engineering organization thrives or spirals into complexity.

A simple map of the Ops universe

Across DevOps, CloudOps, DataOps, MLOps, and every other XOps‑Ops variation, each discipline anchors on one shared goal: Make engineering succeed in production — reliably, securely and at scale.

Yet the disciplines approach this from different domains: code, infrastructure, data, ML models, security, cost, and platforms.

Engineering Ops Roles — A Simplified Overview

The unifying theme behind every Ops discipline: Operate-as-Code

After examining all these Ops disciplines, one truth becomes obvious:

Every Ops discipline is simply “Operate-as-Code” applied to a different domain.

That’s the shared DNA — the foundation that determines whether any Ops practice truly drives engineering success.

Here’s what Operate-as-Code means in practice:

Declarative everything (infrastructure, policies, budgets, SLOs)

The desired state of systems — infra configs, cost guardrails, compliance rules, model versions, data pipelines — must be expressed as code and versioned in Git.

GitOps is the gold standard here: auditable, reviewable, reversible, automatable.

Ruthless automation

High‑performing Ops disciplines automate as much as possible:

• Deployment pipelines
• Security scanning (DevSecOps)
• Cost checks (FinOps)
• Drift detection (MLOps/LLMOps)
• Infra provisioning (DevOps/CloudOps)
• Data quality checks (DataOps)

The highest‑performing DevOps teams are the most automated ones.

Observability and continuous feedback

In today's high-performing operational environments, each Ops discipline relies on its own set of telemetry signals — critical data points that provide actionable insights into the health, performance and security of systems. Telemetry forms the backbone of automation, enabling teams to establish continuous feedback loops that drive improvements and ensure operational excellence.

• Site Reliability Engineering (SRE): SRE teams monitor Service Level Indicators (SLIs) and Service Level Objectives (SLOs) to quantify system reliability and availability.
• FinOps: Cost signals are central to FinOps, providing visibility into cloud spend and resource utilization.
• DataOps: Data lineage and quality metrics are essential for tracking the provenance and integrity of data as it moves through pipelines.
• MLOps: Model drift telemetry monitors changes in machine learning model behavior over time. For instance, a shift in prediction accuracy or input data distribution can signal that a model is no longer performing as expected.
• CloudOps: Infrastructure health telemetry includes CPU utilization, memory consumption, network latency, and application uptime.
• DevSecOps: Security posture telemetry tracks vulnerability scans, compliance status and access anomalies.

Observability acts as the operational nervous system, unifying these telemetry streams to provide holistic visibility across all domains. By connecting automated checks, continuous feedback, and policy enforcement, observability enables teams to detect issues early, respond rapidly and drive ongoing optimization. This centralized approach ensures that standards are embedded into automation, supporting both agility and reliability as organizations scale their operations.

Policy-as-code and guardrails‑as‑code

Standards must be embedded into automation:

• Security policies (DevSecOps)
• Access and governance (CloudOps)
• Tagging and resource rules (FinOps)
• Architecture constraints (Platform Engineering)

Policy-as‑Code ensures consistency without slowing teams down. By codifying standards and guardrails directly into automated workflows, organizations can enforce security, governance and compliance uniformly across all environments. This approach enables rapid, self‑service operations while embedding best practices into every deployment, fostering both agility and reliability.

Self‑service platforms (paved roads)

Platform Engineering is booming because organizations realize:

If you give developers paved roads, they stop driving off cliffs.

Self-service environments reduce friction, increase velocity and enforce standards invisibly. By providing developers with ready-made, well-defined pathways — often referred to as “paved roads” — organizations empower teams to deliver faster without having to constantly navigate complex compliance or operational requirements. This seamless integration of standards into the platform ensures that best practices are automatically followed, enabling both agility and reliability in every deployment.

Closed feedback loops

Every Ops discipline thrives when real-world signals continuously shape engineering decisions.

• Reliability → capacity planning
• Security → SDLC improvements
• Costs → architecture choices
• ML drift → retraining
• Data quality → pipeline fixes

Teams that close feedback loops grow stronger with every deployment.

Why this matters more than ever in 2026

We are in an era of:

• Skyrocketing cloud costs
• AI and LLM proliferation
• Massive data pipelines
• Accelerating security threats
• Complex distributed systems
• Rising customer expectations

Engineering success now depends on your ability to operate:

• Reliably, securely, cost‑effectively, at scale
• With automation, clarity, governance, and continuous learning

This is why so many Ops roles emerged. And yet, beneath all the noise …

Modern engineering organizations win when they operationalize everything — as code. Everything else is branding.

A simple Ops maturity model

Here’s a practical roadmap that organizations can adopt internally:

• Level 1: Tool‑driven Ops – Manual scripts, siloed dashboards, reactive operations
• Level 2: Pipeline‑driven Ops – CI/CD everywhere, environment parity, automated tests
• Level 3: Policy‑driven Ops – GitOps, FinOps guardrails, security gates, IDP adoption
• Level 4: Outcome‑driven Ops – SLOs and error budgets, AI-powered automation, proactive cloud governance, automated model retraining, ‑powered automation, proactive cloud governance, automated model retraining, business‑aligned KPIs

When organizations climb these levels, “XOps” labels stop mattering — engineering starts to run like a well‑designed machine.

Closing thoughts

In 2026’s fast‑changing engineering world, “Ops” roles are not a trend — they’re a recognition of reality. Software isn’t just built. It’s operated, continuously, as a living system.

The more domains we touch — cloud, AI, data, security, cost — the more Ops disciplines we create. But they all point back to the same north star:

Operate-as-Code is the true DNA driving high-performance engineering.

Teams that embrace this truth will see systems scale, costs stabilize, delivery accelerate, and reliability strengthen.

Everything else is just implementation detail.

Note: Saravana Mohan, Manager of Content Security Engineering at Barracuda, also contributed to this article.

Note: This article was first published on LinkedIn.