Data Engineer (Python / PySpark)

Experience supporting AML, Transaction Monitoring, Investigations, Sanctions screening, Fraud, or similar risk/compliance functions. Partner with business and technical stakeholders to translate requirements into data models, mappings, and curated datasets.

Our Client, an IT Services and Consultant company, is looking for a Data Engineer (Python / PySpark) for their Charlotte, NC location.
 
Responsibilities:

• Build and maintain batch and/or streaming data pipelines supporting financial crimes initiatives.
• Develop data transformations using Python + PySpark and optimize performance for large datasets.
• Apply strong understanding of Apache Spark architecture (executors, partitions, shuffles, joins, caching) to improve performance
• Partner with business and technical stakeholders to translate requirements into data models, mappings, and curated datasets.
• Support ingestion from multiple sources (transactional systems, case management, reference data, etc.).
• Implement data quality checks, reconciliation, and controls to ensure auditability and reliability.
• Contribute to modernization efforts (legacy → in-house build) including migration planning and redesign.
• Create documentation for pipelines, logic, and operational runbooks.
• Work within Agile delivery (Jira), supporting sprint execution and delivery timelines.

 
Requirements:

• 5+ years of experience in data engineering / ETL / data platform development
• Strong hands-on development in:
• Python
• PySpark / Apache Spark
• Advanced SQL
• Experience working with large-scale data sets and performance tuning.
• Strong understanding of data concepts: data modeling, lineage, metadata, governance
• Experience supporting regulated environments with emphasis on controls and audit readiness
• Strong communication skills (ability to work with both engineering + business partners)
• Preferred Skills (Nice to Have):
• Experience running PySpark workloads on Google Cloud Platform (GCP)
• Dataproc, BigQuery, Google Cloud Storage (GCS), etc.
• Experience with cloud native Big Data platforms
• Knowledge of data governance, security, and compliance practices
• Experience with CI/CD pipelines for data engineering workloads
• Orchestration: Airflow (or similar scheduling tools)
• Streaming: Kafka
• Lakehouse/Warehouse: Databricks / Snowflake / BigQuery
• CI/CD + DevOps: Git, pipelines, automation, release management
• Data governance/security: encryption, access controls, data masking, PII handling
• Prior Financial Crimes domain: AML / sanctions / fraud / investigations / KYC
• Domain Experience (Highly Valued):
• Experience supporting AML, Transaction Monitoring, Investigations, Sanctions screening, Fraud, or similar risk/compliance functions.
• Familiarity with regulatory expectations and strong documentation discipline.
• Years of Experience: 9.00 Years of Experience

 
Why Should You Apply?

• Health Benefits
• Referral Program
• Excellent growth and advancement opportunities