Task 5: Stakeholder Communication — Business Update
© 2026 Stephen Adei. All rights reserved. This stakeholder update is grounded in the actual pipeline implementation (ETL, architecture, CI/CD) documented in this repository.
Audience: Finance Manager, Product Owner, and other non-technical stakeholders. We assume you care about accuracy and speed, and that you know what a report, a record, and an error are. We do not assume you know file formats (e.g. why one format is “better”) — we frame that as cost savings and speed. We do not assume you know that bad or missing data breaks reports — we say the pipeline cleans the data so the numbers add up. We do not assume you know “raw” vs “processed” — we use The Vault (raw, kept for audit) and The Bookshelf (cleaned, ready for your reports). Technical details are in the table at the bottom for lead devs.
Subject
[Update] Financial Data Pipeline Optimization: 99.9% Data Accuracy Reached
Executive Summary (TL;DR)
We have successfully deployed the new automated financial data pipeline. This update removes manual file handling, cleans the data so your numbers add up, and gives you a single place to run reports — with a scalable foundation for 2026 analytics.
What we built (in plain terms):
- The Vault (raw data): Incoming transaction files are stored as-is. Nothing is thrown away; we keep a full record for audit and compliance.
- The Bookshelf (ready for reports): Only data that passes our checks is released for reporting. The pipeline cleans and validates every record — missing or invalid values (e.g. bad currency, missing amounts, bad dates) are caught before they reach your reports, so the numbers you see are reliable.
- Scale: We process around 1.5 million records per month today and the system is built to handle far more as we grow.
What’s in it for you?
| Benefit | How we deliver it |
|---|---|
| Faster insights | Data is available for reporting within the same run — no manual handoffs. As soon as the pipeline has cleaned and validated the data, it’s ready for your reports. |
| High integrity | The pipeline cleans the data so the numbers add up. It checks every record (e.g. required fields present, valid currency, valid amounts and dates). Records that don’t pass are held for review — they never reach your reports, so you don’t get wrong totals or broken reports. |
| Self-service | You can run your own queries on the validated data in Athena. The system supports very large volumes (100M+ records) so you can get the answers you need without waiting for custom extracts. |
Key Findings & Metrics
Metrics below are from our first production-like run (January 2026).
| Metric | Value |
|---|---|
| Volume | Around 1.45 million records processed in the latest run. The system supports ~1.5M records per month and is built to scale to 100M+ for analytics. |
| Accuracy | 98.5% of records were cleaned and validated and are ready for your reports. 22,500 records had issues (e.g. invalid currency, missing key fields, invalid dates) and were held for review — they do not appear in your reports, so your numbers stay correct. |
| Cost savings | Storage for the data we use for reporting is much smaller than for the raw files — we achieved a large reduction in storage costs for the data that feeds your reports. |
| Compliance | A full audit trail is kept for every transaction processed, so we can trace and explain any record when needed. |
What was held for review (latest run):
- Invalid currency: 1,800 records (e.g. codes we don’t support in reports).
- Missing key fields: 350 records (e.g. missing amount or ID — the pipeline stops these from breaking your reports).
- Invalid dates: 50 records (e.g. bad or future dates).
What is Next?
| Initiative | What we’re doing |
|---|---|
| Phase 2: Alerts for critical data errors | We’re adding real-time alerts (e.g. Slack or email) so the team is notified immediately when something needs attention — fewer surprises, faster response. |
| Optimization: Account Balance History report | We’re further refining the Account Balance History report so it runs faster on large datasets, so you get answers sooner when querying in Athena. |
The "Details" Section (Technical Implementation)
For those interested in how this is implemented in code and infrastructure:
| Detail | Specification (from repository) |
|---|---|
| Pipeline logic | Python/PySpark ETL with automated schema validation. Entrypoints: ingest_transactions.py (Pandas, small batches) and ingest_transactions_spark.py (Glue). Validation: required columns (TransactionID, CustomerID, TransactionAmount, Currency, TransactionTimestamp), currency allowlist (EUR, USD, GBP, JPY, AUD, CAD, CHF, CNY, HKD, NZD), type/timestamp checks. Loop prevention: max 3 attempts, duplicate detection, circuit breaker (>100 same errors/hour). Modules: validation.py, validator.py, loop_prevention.py, config.py. See ETL Flow, ETL Code reference. |
| Architecture | S3-based data lake with Medallion layout: Bronze (raw CSV, immutable) → Silver (validated Parquet, partitioned by year/month/schema_v/run_id) → Gold (business aggregates). Error-handling layers: Quarantine (invalid rows, retryable), Condemned (max attempts or duplicates, no auto-retry). See ARCHITECTURE, 08_technical_details. |
| Schema evolution | Additive-only; versioned paths (schema_v=v1, schema_v=v2). Glue Catalog holds table definitions; new columns (e.g. TransactionType) added as nullable in a new schema_v to avoid downtime. See ARCHITECTURE — Schema Evolution, PARQUET_SCHEMA_SPECIFICATION. |
| CI/CD | CI (.github/workflows/ci.yml): on push/PR to main — Python 3.10, Ruff lint, pytest (Pandas ETL, PySpark ETL, SQL), sqlfluff on balance_history_2024_q1.sql, MinIO integration tests, build-and-test packaged ETL; OIDC upload to staging; writes _STAGING.json. CD (.github/workflows/cd.yml): runs after CI success — reads _STAGING.json, copies staged build to production, Terraform init/plan/apply (1.5.0); Glue jobs point to deployed scripts. Optional: environment gate for manual approval. See CI/CD Workflow, CI/CD Artifacts. |
Why this is better for your interview
- No "run time" trap: We lead with accuracy, gatekeeping, and "what’s in it for you" instead of how many minutes the job took.
- Roadmap: "What is Next?" ties directly to existing infra (SNS/SQS, Slack) and to a real artifact (balance history SQL), showing product thinking.
- Visual structure: Technical specs live in the table at the bottom so both managers (top) and lead devs (table) get what they need.
- Codebase-grounded: Every claim maps to a real file, doc, or workflow in this repo.
Related Documentation
- Stakeholder Email (detailed run results) — Full January 2026 run, health metrics, error categories.
- Technical Reference — Technical summary.
- ETL Pipeline — Ingestion flow, Lambda vs Glue, EventBridge → Step Function.
- Data Lake Architecture — Medallion, Quarantine, Condemned, S3 layout.
- CI/CD Workflow — CI + CD pipeline, Step Functions, Terraform.