Data Engineer with Risk & Fraud (Remote)

Job Description: • Experience with Risk Analysis and Fraud Detection is mandate. • • Experience in data engineering, with at least 3 years working hands-on with PySpark, Azure Data Factory, and Python in production environments. • • Strong background in designing and implementing large-scale data pipelines, including batch and real-time ingestion for risk, fraud, or financial datasets. • • Deep experience with PySpark for distributed data processing, data quality validation, data enrichment, feature engineering, and fraud-signal extraction. • • Solid expertise in Azure Data Factory for orchestrating complex ETL/ELT workflows across multiple data sources. • • Proficiency in Python for data processing, automation, API integration, anomaly-detection scripts, and model-ready dataset preparation. • • Strong SQL skills, including query optimization, performance tuning, and working with both relational and non-relational stores such as Cosmos DB, Kusto, or ADLS. • • Good understanding of data warehousing, dimensional modeling, and data quality frameworks used in risk scoring and fraud detection systems. • • Exposure to the broader Azure ecosystem such as Synapse, Databricks, EventHub, Service Bus, Key Vault, Functions, Monitor, Log Analytics, and other platform components used in risk and fraud architecture. • • Familiarity with streaming architectures and patterns such as event-driven pipelines, near real-time scoring, and anomaly monitoring. • • Experience working with high-volume, sensitive data while adhering to security, compliance, and privacy guidelines. • • Strong analytical and problem-solving abilities, with the ability to troubleshoot complex data pipeline issues in a risk or fraud context. • • Effective communication skills to work with engineering, analytics, and fraud operations teams.