Hybrid Onsite Lake Mary, FL (Greater Orlando Region) Data Architect Risk & Compliance, Sanctions Screening, and AML We are seeking a Data Architect with expertise in Risk & Compliance, Sanctions Screening, and Anti-Money Laundering (AML) to lead the design and development of scalable data solutions that support financial crime detection and regulatory compliance. This role requires a strong architectural mindset to design high-performance data platforms, optimize data pipelines, and ensure compliance with regulatory requirements. The ideal candidate should have deep experience with big data architectures, cloud platforms, and data security best practices while collaborating with cross-functional teams. Key Responsibilities Data Architecture & Solution Design - Define and implement end-to-end data architecture for risk and compliance systems, ensuring scalability, security, and performance.
- Design data models, pipelines, and frameworks to support sanctions screening, AML transaction monitoring, KYC, fraud analytics, and regulatory reporting.
- Develop high-availability and fault-tolerant data processing architectures that handle large-scale, real-time financial transactions.
Big Data & Cloud Strategy - Architect and implement big data solutions using Apache Spark, Hadoop, Databricks, or Snowflake.
- Leverage cloud platforms (AWS, Azure, Google Cloud Platform) for data storage, transformation, and analytics, integrating services like AWS Glue, Redshift, Synapse, or BigQuery.
- Design real-time streaming data architectures using Kafka, Flink, or similar technologies for fraud detection and compliance monitoring.
Data Governance & Compliance - Ensure data integrity, quality, security, and privacy across all risk and compliance systems.
- Implement data governance frameworks and compliance measures aligned with global financial regulations (FATF, OFAC, FinCEN, GDPR, etc.).
- Establish audit trails, access controls, and data encryption to meet security and regulatory standards.
Performance Optimization & Automation - Optimize query performance, indexing strategies, and distributed computing for large-scale datasets.
- Automate ETL/ELT pipelines, data validation, and reconciliation processes to improve efficiency and accuracy.
- Implement AI/ML-driven anomaly detection for fraud and risk management.
Collaboration & Leadership - Work closely with Risk, Compliance, AML, and Fraud teams to define data requirements and align technology solutions with business objectives.
- Manage the product backlog through execution and delivery.
- Guide and mentor data engineers, ensuring best practices in data architecture, engineering, and security.
- Engage with executive leadership and stakeholders, providing technical recommendations and strategic insights.
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