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Stakeholder Analysis & Concerns

Overview

This document makes implicit stakeholder focus explicit, satisfying arc42 Chapter 1.4 (Stakeholders). It maps each stakeholder's key concerns to where those concerns are addressed in the documentation, providing confidence assessments for each mapping.

Scope: This solution is the OLAP analytics layer; Ohpen core banking (OLTP) is upstream and out of scope (Scope & Assumptions).

Extended context: Executive Summary, Design Decisions Summary.

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Stakeholder Matrix

Stakeholder	Key Concerns	Where Addressed	Confidence Assessment
Interview Panel	Technical depth, architectural judgment, production readiness, explicit trade-off thinking	README (time-based navigation paths), Executive Summary (business case), Design Decisions Summary (trade-off analysis), Data Lake Architecture (734-line depth)	High confidence: Depth and trade-off analysis are documented; explicit "unspoken operational challenges" framing is present.
Platform / Cloud Engineer	Infrastructure-as-code, cost optimization, scalability, AWS service selection, OIDC authentication, Terraform	CI/CD Workflow (Terraform, OIDC), Tooling & Controls (service rationale), Design Decisions Summary (serverless vs always-on, lifecycle policies), Data Lake Architecture (partition pruning 95% reduction)	High confidence: Explicit AWS Well-Architected Framework mapping, cost anomaly alarms, lifecycle policies show production engineering.
Data Engineer	ETL logic, transformation cardinality, data quality, testing, PySpark optimization, validation rules, quarantine workflow	ETL Flow (pseudocode + diagrams), PySpark Implementation Summary (performance optimizations), Testing Guide (validation strategy), Data Lake Architecture (medallion layers, error handling)	High confidence: Dual implementation strategy (Pandas + PySpark), explicit validation rules, quarantine workflow.
Data Analyst / BI Developer	Query performance, partition pruning, schema stability, Gold layer aggregations, month-end reporting accuracy	SQL Breakdown (partition pruning, window functions, carry-forward logic), Data Lake Architecture (Gold layer 1:N pattern, schema evolution), Parquet Schema Specification (schema contract)	High confidence: Month-end query optimization (100M rows), partition strategy aligned with reporting queries.
Security / Compliance / Audit	Encryption, access control, audit trail, immutability, perpetual retention, CloudTrail logging, KMS keys	Tooling & Controls (KMS, CloudTrail, IAM), CI/CD Workflow (OIDC keyless auth), Data Lake Architecture (immutable Bronze, Condemned perpetual retention), Traceability Design (run identity propagation)	High confidence: "Traceability and auditability have top priority" statement in Design Decisions Summary. Quarantine audit trail, CloudTrail selective data events.
Product / Business (Finance)	Month-end accuracy, business contracts, stakeholder communication, reporting SLAs, data contract guarantees	SQL Breakdown (balance history query), Data Lake Architecture (Gold layer ownership = Finance), Executive Summary (promotion gate criteria), Governance Diagrams (approval workflows)	Partial: Business concerns addressed but not explicitly called out. Missing: SLA definitions, data contract documentation, business metric glossary.
SRE / Operations	Observability, alerting, rollback procedures, failure recovery, incident response, run identity correlation	Traceability Design (run identity, CloudWatch metrics), Audit & Notifications (SNS/SQS alerting), CI/CD Workflow (automated rollback), Data Lake Architecture (failure mode analysis), Runtime Scenarios (operational flows)	High confidence: Run identity propagation, metric dimensioning (RunId, ExecutionArn), retry logic, circuit breaker patterns.

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Stakeholder Journeys

Reviewer Journey (5-30 minutes)

5-minute path (Decision-maker):

1. README → Quick navigation guide
2. Executive Summary → Business case and solution overview
3. System Architecture Overview → End-to-end system diagram

Verdict: Sufficient for decision-maker: business case, solution overview, end-to-end system diagram.

15-minute path (Technical reviewer):

1. 5-minute path above
2. Design Decisions Summary → Trade-off thinking
3. Data Lake Architecture → Medallion design

Verdict: Sufficient for technical reviewer: trade-off thinking, medallion design, error handling layers.

30-minute path (Deep technical review):

1. 15-minute path above
2. ETL Flow → Implementation details
3. SQL Breakdown → Query optimization patterns
4. CI/CD Workflow → Deployment automation

Verdict: Sufficient for deep technical review: implementation details, optimization patterns, deployment automation.

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Platform Engineer Journey (Implementation-focused)

Entry Point: Tooling & Controls (service inventory)

Deep Dive Path:

1. CI/CD Workflow → OIDC authentication, Terraform infrastructure
2. Data Lake Architecture → S3 lifecycle policies, partition strategy
3. Traceability Design → CloudWatch metrics, run identity

Code References:

• IAM Security Design → IAM policies
• AWS Terraform Examples → Infrastructure code

Verdict: Clear path. Navigation structure supports this journey.

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Data Engineer Journey (ETL-focused)

Entry Point: ETL Flow (pipeline overview)

Deep Dive Path:

1. PySpark Implementation Summary → Optimization strategies
2. Testing Guide → Validation and testing approach
3. Data Lake Architecture → Medallion layers and error handling

Code References:

• ETL Complete Reference → Full implementation
• ETL Code → Python code

Verdict: Clear path. Hub-to-Reference linking supports discovery.

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Analyst Journey (Query-focused)

Entry Point: SQL Breakdown (query optimization)

Deep Dive Path:

1. Data Lake Architecture → Partition strategy, Gold layer structure
2. Parquet Schema Specification → Schema contract

Code References:

• SQL Complete Reference → Extended SQL documentation
• SQL Examples → Query patterns

Verdict: Clear path. Schema documentation is strong.

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Stakeholder Gap Analysis

Stakeholder	Missing Content	Impact	Recommendation
Product / Business	- SLA definitions (e.g., "Month-end reports available by X hours after month close") - Data contract documentation (what Gold layer guarantees to consumers) - Business metric glossary (e.g., "new_balance" definition)	Medium: Business case covers technical depth but does not explicitly frame business commitments.	OPTIONAL: Add DATA_CONTRACTS.md page defining: - Gold layer schema guarantees (backward compatibility policy) - Query performance SLAs - Promotion gate criteria as business contract Not critical for interview but valuable for production.
Incident Responders	- Runbook index (backfill, rollback, quarantine review, schema evolution) - Troubleshooting decision trees - Common failure scenarios with remediation	Medium: PRODUCTION_READINESS.md exists (referenced but not published). Operational procedures scattered across ARCHITECTURE, ETL_FLOW, CI_CD_WORKFLOW.	ADDRESSED: Runbooks hub page consolidates operational procedures.
Onboarding Engineers	- Consolidated glossary - "How do I..." task-based guides - Development environment setup	Medium: Terminology scattered across pages (TRACEABILITY, ARCHITECTURE). No quick-start guide for contributors.	ADDRESSED: Glossary consolidates terminology.

Verdict: Core stakeholders well-served. Gaps are production handoff concerns, not business case deficiencies.

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See also

• Executive Summary - Business case and solution overview
• Design Decisions Summary - Architectural trade-offs and rationale
• Data Lake Architecture - Complete architecture design
• ETL Flow - ETL pipeline implementation
• Runtime Scenarios - Operational flows