The Problem: The "it works on my machine" trap. As data teams grow, ad-hoc processes that worked for a single engineer crumble under the weight of production requirements. Teams often know they need to improve, but they lack a unified definition of success. Without clear standards, it is impossible to measure progress.
This talk presents a comprehensive Operational Excellence Maturity Pyramid, designed to guide data teams from chaos to stability. We will explore a 5-level classification system (Struggling, Basic, Decent, Strong, and Mastery) applied across three foundational pillars of data engineering.
Struggling: Manual scheduling, no dependency management, lack of idempotency.
Mastery: Dynamic DAGs, event-driven triggers, automated backfills, modular infrastructure-as-code, and self-healing pipelines and more.
Struggling: No testing program; quality issues are discovered by stakeholders downstream.
Mastery: Comprehensive coverage (Write-Audit-Publish patterns), automated anomaly detection, and "circuit breakers" that stop bad data before it hits the warehouse.
Struggling: Undefined targets; "best effort" delivery.
Mastery: Fully measurable SLIs (Service Level Indicators), defined Error Budgets, and automated alerting on burn rates.
-- What You Will Learn: This session is not just theoretical; it is a practical guide for data engineers, platform leads, and managers. By the end of this talk, you will be able to:
Audit your current stack: Use the provided scorecard to classify your team's maturity level in each pillar.
Identify gaps: Understand exactly why you are stuck at the "Basic" or "Decent" levels.
Plan your roadmap: Walk away with actionable steps to advance to the next level, turning your data operations into a competitive advantage rather than a maintenance burden.
