01
Understand the business rules
Collaborate with domain experts to understand the DPD logic, rule dependencies and expected financial results before changing the implementation.
Selected Engineering Work · Project 02
High-Performance Financial Data Pipeline
Optimizing complex financial logic without changing its business meaning.
Optimized a business-critical SQL pipeline used for days-past-due calculations, credit-risk analysis and financial reporting, reducing execution time by 45% through earlier filtering, improved join strategies and removal of redundant processing.
The challenge
Performance problems caused by repeated processing, not extreme data volume.
The DPD pipeline implemented approximately 40 business rules. Many of those rules depended on the same intermediate result, but the existing implementation repeatedly applied similar joins and transformations to substantially the same working dataset.
The source data itself was not exceptionally large. The execution time grew because inefficient SQL joins, insufficient early filtering and repeated transformations multiplied the amount of work performed across the rule set.
- Identify joins that processed more data than required.
- Reduce datasets before expensive downstream operations.
- Reuse common intermediate results instead of repeatedly rebuilding them.
- Improve performance without changing the intended financial calculation.
Optimization architecture
Move from repeated rule processing to a shared, reduced working dataset.
The optimization focused on reducing the amount of data processed before the business rules were evaluated and eliminating repeated work around common dependencies.
Before
Source data
Input entered the pipeline with limited early reduction.
Repeated work
Inefficient joins
More data than necessary participated in downstream SQL operations.
Repeated work
Shared data reprocessed
Common intermediate results were repeatedly evaluated across business rules.
Impact
~4 hour runtime
Accumulated inefficiencies drove unnecessary processing and compute usage.
After
Source data
Processing starts by reducing the working dataset as early as possible.
Step 1
Early filtering + better joins
Only relevant records flow into expensive operations; broadcast joins are used where appropriate.
Step 2
Shared intermediate result
Common preparation is reused rather than repeatedly reconstructed for each rule.
Result
Business rules + DPD output
Approximately 40 rules operate on a more efficient processing flow.
Engineering approach
Optimize the execution path while preserving business correctness.
The work combined SQL performance engineering with close collaboration around domain-specific financial logic.
02
Identify repeated processing
Trace where approximately 40 rules repeatedly depended on the same intermediate result and locate unnecessary recomputation.
03
Filter data earlier
Move filtering upstream so joins and transformations work with only the records required by downstream calculations.
04
Improve join strategy
Rework inefficient SQL joins and use broadcast joins where appropriate for smaller datasets and lookup-style relationships.
05
Remove redundant transformations
Consolidate common processing paths and eliminate repeated transformations that generated equivalent intermediate results.
06
Validate with PyTest
Use controlled input datasets and business-defined expected outputs to confirm that optimized SQL logic preserved the intended calculation.
Testing strategy
Performance improvements had to preserve the expected DPD result.
PyTest-based unit tests were built around specific business scenarios. Each test supplied controlled input data and verified that the SQL output matched the result expected by the business.
This provided a safety net for refactoring: join strategies, filtering and shared processing could be improved while the expected financial behavior remained explicit and testable.
- Controlled inputs represented defined business scenarios.
- Expected outputs captured the result the DPD logic was required to produce.
- Optimized SQL output was checked against those expected results.
Key engineering decisions
Changes driven by data flow, domain understanding and correctness.
Optimize shared dependencies
Common intermediate results were treated as shared dependencies instead of repeatedly rebuilding similar processing paths for individual business rules.
Reduce data before expensive work
Filtering moved earlier in the pipeline so joins and transformations operated on smaller working datasets and consumed less compute.
Preserve business meaning
Technical changes were made only after understanding the financial rules and were validated with business-defined input and expected-output tests.
Outcome
45% faster execution with less unnecessary processing.
The optimization reduced execution time by 45% from an original runtime of approximately four hours. Earlier filtering, improved join strategies and the removal of redundant transformations reduced the amount of data processed and improved compute efficiency. PyTest-based unit tests helped ensure that the optimized SQL logic continued to produce the business-expected DPD results.