I’m designing a large-scale deterministic analytics engine and would appreciate architectural + cost/effort advice from people who’ve built similar systems.

The core challenge: • ~1,200 domain-specific metrics • All rule-based (no ML), fully deterministic • Metrics share common primitives but differ in configuration • Metrics combine into composite indices • Outputs must be auditable and reproducible (same inputs → same outputs) • I want metrics definable declaratively (not hard-coded one by one)

The system ingests structured event data, computes per-entity metrics, and produces ranked outputs with full breakdowns.

I’m specifically looking for guidance on: • Architectures for large configurable rule/metric engines • How to represent metric definitions (DSL vs JSON/YAML vs expression trees) • Managing performance without sacrificing transparency • Avoiding “1,200 custom functions” antipatterns • What you’d do differently if starting this today

Cost / effort sanity check (important): If you were scoping this as a solo engineer or small team, what are the biggest cost drivers and realistic milestones? • What should “Phase 1” include to validate the engine (e.g., primitives + declarative metric format + compute pipeline + 100–200 metrics)? • What’s a realistic engineering effort range for Phase 1 vs “all 1,200” (weeks/months, 1–2 devs vs 3–5 devs)? • Any common traps that make cost explode (data modeling mistakes, premature UI, overengineering the DSL, etc.)?

I’m not looking to hire here — just trying to sanity-check design decisions and expected effort before implementation.

Thanks in advance for any insight.