Architect

Long Description

Job Description: AML Solution Architect (Actimize & Data Quality)

Position Title

AML Solution Architect – Actimize Data Quality & Modernization

Location Remote (Ocasional Travel)

Experience

12+ Years

Role Overview

We are seeking an experienced AML Solution Architect with deep expertise in NICE Actimize, AML monitoring, data architecture, and data quality management to review the current AML ecosystem, identify process and control gaps, and define strategic solutions that improve data quality, regulatory compliance, and operational effectiveness.

The ideal candidate will work closely with AML Operations, Compliance, Data Warehouse, ETL, and Technology teams to assess the end-to-end data lifecycle from source systems through Actimize and recommend scalable architecture patterns, reconciliation controls, monitoring frameworks, and modernization opportunities.

Key Responsibilities

AML Architecture Assessment

• Review current AML technology architecture, including source systems, Data Warehouse, ETL processes, ODS, staging layers, and Actimize integration.
• Analyze customer, account, transaction, KYC, and watchlist data flows feeding Actimize.
• Identify architectural weaknesses, bottlenecks, and operational risks impacting AML monitoring effectiveness.
• Evaluate current-state data lineage and traceability across AML data pipelines.

Actimize Solution Design

• Design optimized data ingestion frameworks for NICE Actimize.
• Define source-to-Actimize reconciliation and validation strategies.
• Review Actimize scenarios, alert generation processes, and data dependencies.
• Recommend scalable architecture to improve completeness and integrity of AML monitoring data.
• Partner with AML stakeholders to enhance Actimize processing efficiency and data coverage.

Data Quality & Controls Framework

• Design enterprise-grade AML Data Quality Framework.
• Define controls for:
• Completeness
• Accuracy
• Consistency
• Timeliness
• Uniqueness
• Referential Integrity
• Establish data quality scorecards and dashboards.
• Create monitoring mechanisms for:
• Missing records
• Data drift
• Schema changes
• Failed loads
• Filter impact analysis
• Exception management

Long Description

Data Governance & Compliance

• Collaborate with Compliance, Audit, Risk, and AML Operations teams.
• Define governance standards supporting regulatory expectations.
• Establish audit-ready lineage and traceability processes.
• Prepare architecture documentation supporting MRA/MRIA remediation activities.
• Ensure solution designs align with AML regulatory obligations and model governance requirements.

Modernization & Transformation

• Recommend modernization roadmap for AML data processing.
• Evaluate opportunities involving:
• ODS optimization
• Real-time processing
• Event-driven architectures
• Cloud-based AML data platforms
• Metadata-driven controls
• Automated DQ frameworks
• Drive reusable architecture patterns and AML best practices.

Stakeholder Management

• Act as the primary liaison between:
• AML Business Teams
• Compliance
• Data Warehouse Teams
• ETL Teams
• Actimize SMEs
• Enterprise Architecture Teams
• Facilitate workshops and architecture reviews.
• Present findings and recommendations to senior leadership.

Area(s) of responsibility

Required Skills

AML Domain

• Strong knowledge of:
• AML Transaction Monitoring
• KYC/CDD
• Sanctions Screening
• Watchlist Processing
• SAR/STR Reporting
• Regulatory Compliance Programs

Actimize Expertise

• NICE Actimize Transaction Monitoring
• Actimize Data Model
• Actimize IFM/RCM/AIS knowledge
• Alert Management
• Case Management
• Scenario Data Requirements
• Actimize Batch Processing Architecture

Data Architecture

• Data Warehousing Concepts
• ETL/ELT Architectures
• Oracle Database
• Data Modeling
• Data Lineage
• Metadata Management
• Master Data Management

Data Quality

• Data Profiling
• Data Reconciliation
• Data Validation Frameworks
• Root Cause Analysis
• Data Observability
• Data Governance

Technical Skills

• SQL
• Oracle
• Ab Initio / Informatica / DataStage
• Python (preferred)
• Databricks (preferred)
• Kafka/Event Streaming (preferred)
• Cloud Platforms (AWS/Azure preferred)