Description :

Role : Senior Software Engineer Python (Computation & Rules Engine)

Role Overview :

We are building a high-performance, deterministic computation engine in Python that executes complex, versioned rules and calculations over large, structured datasets. This engine powers financial availability, eligibility, and risk calculations and must be correct, explainable, auditable, and fast. Build a high-performance, deterministic Python computation engine that executes complex rules over large datasets with absolute correctness and explainability.

This is a pure backend engineering role focused on algorithms, data processing, system design, and performancenot UI and not business analysis.

Core Engineering Responsibilities :

Design and implement pure computation modules in Python with :

- Deterministic outputs

- Idempotent execution

- Strong guarantees around precision and reproducibility

Build a rule execution framework supporting :

- Declarative rule definitions

- Versioning and effective dating

- Rule dependency graphs

- Short-circuiting and prioritization

Implement high-volume data pipelines to :

- Load, validate, normalize, and transform large datasets

- Execute calculations efficiently in batch and incremental modes

Optimize performance across :

- CPU usage

- Memory footprint

- IO patterns

- Parallel execution (where appropriate)

Design explainability artifacts :

- Structured calculation traces

- Rule evaluation graphs

- Intermediate state snapshots

Implement numerically safe calculations :

- Decimal arithmetic

- Explicit rounding strategies

- Stable aggregation and reconciliation logic

Build robust testing infrastructure :

- Unit tests for individual rules

- Property-based tests for invariants

- Golden dataset regression tests

Design APIs and internal interfaces for :

- Batch execution

- Streaming / delta-based recalculation

- Downstream consumers (services, workflows)

Required Technical Skills :

- Strong proficiency in Python (5+ years preferred)

Deep experience with data-intensive systems, including :

- pandas / Polars

- NumPy

- PyArrow

- SQL-backed pipelines

Strong understanding of :

- Algorithmic complexity

- Memory-efficient data structures

- Vectorized vs row-based execution tradeoffs

Experience building :

- Rules engines

- Calculation engines

- Compilers / interpreters / evaluators

Solid grasp of :

- Deterministic system design

- Idempotency

- Fault-tolerant batch processing

Strong testing discipline, including :

- pytest

- Property-based testing (Hypothesis)

- Reconciliation and diff-based validation

Preferred / Advanced Engineering Experience :

- Designing DSLs or configuration-driven execution engines

- Working with large datasets (10M+ rows) or wide schemas

- Parallel and distributed computation (Dask, Ray, Spark)

- Low-latency vs high-throughput compute tradeoffs

- Experience with financial or regulatory systems where correctness > speed

- Database performance tuning (Postgres, Oracle)

- Building internal observability for compute engines