Data Engineer / Data Platform Engineer (Python, PySpark)

This role will support data engineering and platform development for financial crimes use cases (AML, investigations, sanctions, fraud, KYC) by building scalable pipelines, improving data quality, and enabling analytics/reporting and downstream applications. Responsibilities: Financial Crimes Technology team is evolving toward more in-house build capabilities and reducing dependency on legacy vendor/tooling approaches.

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

• Financial Crimes Technology team is evolving toward more in-house build capabilities and reducing dependency on legacy vendor/tooling approaches.
• This role will support data engineering and platform development for financial crimes use cases (AML, investigations, sanctions, fraud, KYC) by building scalable pipelines, improving data quality, and enabling analytics/reporting and downstream applications.
• 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
• 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
• 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
• 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
• Strong hands-on development in:
• Python
• PySpark / Apache Spark
• Advanced SQL

 
Why Should You Apply?

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