Role: Sr Data Engineering Architect
Location: Cleveland, OH (Onsite)
Fulltime Permanent
Job Description
Skill: Sr Data Engineering Architect Fraud Domain
Must Have Technical/Functional Skills:
- Define and implement enterprise data architecture, data models, governance standards, and technology roadmaps for fraud and risk analytics platforms.
- Design and oversee scalable batch and real-time data pipelines, data lakes/lake houses, data warehouses, and cloud-based data solutions.
- Partner with Fraud, Risk, Compliance, and Business teams to deliver solutions supporting fraud detection, transaction monitoring, AML, behavioral analytics, and regulatory reporting.
- Ensure data quality, security, lineage, compliance, performance optimization, and adherence to architecture best practices.
- Lead architecture reviews, mentor engineering teams, and collaborate with stakeholders to drive data modernization initiatives.
Qualifications & Skills:
- 10+ years of experience in Data Engineering, Data Warehousing, Analytics, or Data Management.
- 3+ years of experience as a Data Architect designing enterprise-scale data solutions.
- Strong BFSI, Payments, FinTech, or Fraud/Risk domain experience, including fraud detection, AML, KYC, transaction monitoring, and risk analytics.
- Expertise in Data Architecture, Data Modeling, Data Lake/Lakehouse, Data Warehouse, ETL/ELT, Data Governance, and Metadata Management.
- Hands-on experience with SQL, Python, Spark/PySpark, Databricks, Kafka, Airflow, Snowflake, and modern cloud platforms.
- Knowledge of real-time streaming, event-driven architectures, MDM, Data Mesh, and ML/AI-enabled data platforms.
- Strong leadership, stakeholder management, communication, and solution design skills.
Roles & Responsibilities:
- Define and implement enterprise data architecture, data models, governance standards, and technology roadmaps for fraud and risk analytics platforms.
- Design and oversee scalable batch and real-time data pipelines, data lakes/lakehouses, data warehouses, and cloud-based data solutions.
- Partner with Fraud, Risk, Compliance, and Business teams to deliver solutions supporting fraud detection, transaction monitoring, AML, behavioral analytics, and regulatory reporting.
- Ensure data quality, security, lineage, compliance, performance optimization, and adherence to architecture best practices.
- Lead architecture reviews, mentor engineering teams, and collaborate with stakeholders to drive data modernization initiatives.
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