Streamlining financial data: a secure ETL pipeline.

An auditable ETL for regulatory reporting.

We were engaged by a financial services firm to build a secure, auditable ETL pipeline for processing sensitive financial data. The client needed to consolidate data from multiple source systems (trading platforms, settlement systems, and market data feeds) into a unified data warehouse for predictive analytics and regulatory reporting.

Given the sensitivity of financial data and requirements around transaction integrity, data lineage, and audit trails, the solution demanded enterprise-grade security and compliance controls throughout.

Sensitive data, T+1 deadlines, fragmented sources.

• Data sensitivity: Processing included PII (customer details), financial transaction data, and proprietary trading information requiring encryption, access controls, and audit logging throughout the pipeline.
• Regulatory requirements: The pipeline needed to support MiFID II transaction reporting obligations, requiring complete data lineage from source to report and T+1 reconciliation capabilities.
• Fragmented infrastructure: Data resided across 12 plus source systems with inconsistent schemas, varying update frequencies, and limited API capabilities.
• Operational visibility: The existing manual scripting approach provided no centralised monitoring, making it impossible to track data freshness, identify failures promptly, or demonstrate compliance to auditors.

Orchestrated lineage, encryption throughout, self-service UI.

• Secure orchestration platform: Centralised ETL management with role-based access control, ensuring only authorised personnel can configure pipelines, view sensitive data, or approve production changes.
• Encryption throughout: Data encrypted in transit (TLS 1.3) and at rest (AES-256). Sensitive columns (account numbers, customer identifiers) additionally protected with column-level encryption and tokenisation.
• Complete data lineage: Every record tracked from source extraction through transformation to final load, enabling full audit reconstruction and regulatory reporting compliance.
• Immutable audit logs: All pipeline executions, data modifications, and access events logged to tamper-evident storage with 7-year retention.
• Self-service administration: Intuitive UI for authorised users to add data sources, configure transformations, and monitor pipeline health, with approval workflows for production changes.

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flowchart LR
  subgraph Sources["Source Systems"]
    S1[Trading Platform]
    S2[Settlement]
    S3[Market Data]
    S4[12+ Others]
  end
  subgraph Orchestration["Orchestration"]
    Airflow[Apache Airflow]
    Validate[Great Expectations]
  end
  subgraph Storage["Secure Storage"]
    PG[PostgreSQL 15
Row-Level Security]
    WH[Data Warehouse]
  end
  subgraph Audit["Audit"]
    Lineage[Column Lineage]
    Logs[Tamper-evident Logs]
  end
  S1 --> Airflow
  S2 --> Airflow
  S3 --> Airflow
  S4 --> Airflow
  Airflow --> Validate
  Validate --> PG
  Validate --> WH
  Airflow --> Lineage
  Airflow --> Logs
        

Access, classification, change management, auditor-ready.

• Access control: OIDC integration with corporate identity provider. Role-based permissions (Data Engineer, Analyst, Auditor, Admin) with least-privilege defaults. Segregation of duties between development and production environments.
• Data classification: Automated tagging of sensitive data types (PII, financial, proprietary) with policy-driven handling rules including masking, encryption, and access restrictions.
• Change management: All pipeline modifications require peer review and approval before production deployment. Version-controlled configurations with rollback capability.
• Monitoring and alerting: Real-time dashboards showing pipeline health, data freshness, and anomaly detection. PagerDuty integration for critical failures. SLA tracking for regulatory reporting deadlines.
• Audit support: One-click report generation for auditors showing data lineage, access history, and transformation logic for any record or time period.

A stack built for security and operability.

• Apache Airflow 2.x: DAG-based workflow orchestration with comprehensive logging, retry policies, and SLA monitoring. Custom operators for secure credential handling and audit logging.
• Docker + Kubernetes: Containerised deployment with namespace isolation, network policies, and secrets management via HashiCorp Vault.
• Dask: Distributed computing for parallel data processing and validation. Handles datasets exceeding 100GB with efficient memory management.
• PostgreSQL 15: Primary metadata store with row-level security, encrypted connections, and point-in-time recovery. Audit logging via pgAudit extension.
• React + TypeScript: Type-safe frontend with comprehensive input validation, CSRF protection, and role-aware UI rendering.
• Great Expectations: Data quality framework for automated validation, schema enforcement, and drift detection with alerting.

Measurable efficiency, compliance and reliability.

• 60% reduction in manual data processing time, with automated validation catching data quality issues before they reach downstream systems.
• 100% audit coverage: Complete data lineage for every record, enabling the client to satisfy auditor requests within hours rather than weeks.
• 99.9% pipeline reliability: Automated retry logic, alerting, and self-healing capabilities reduced data delivery failures to near-zero.
• T+1 regulatory compliance: Transaction reporting now meets regulatory deadlines consistently, with automated reconciliation flagging discrepancies for immediate investigation.
• Zero security incidents: 24 months in production with no data breaches, unauthorised access, or compliance violations.