Risk & Fraud Analyst

Position: Risk & Fraud Analyst

Location: Remote

Duration: Full Time

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.