Data Engineer with Risk

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.