Data Platform Engineer

• Build and maintain batch and/or streaming data pipelines supporting financial crimes initiatives. Develop data transformations using Python and PySpark while optimizing performance for large-scale datasets. Partner with business and technical stakeholders to translate requirements into data models, mappings, and curated datasets. Support ingestion of data from multiple sources including transactional systems, case management platforms, and reference data sources. Implement data quality checks, reconciliation processes, and controls to ensure auditability and reliability. Contribute to modernization initiatives involving migration planning, redesign, and replacement of legacy solutions. Create and maintain documentation for data pipelines, transformation logic, and operational runbooks. Work within Agile delivery frameworks using Jira to support sprint execution and delivery timelines.

• 5+ years of experience in data engineering, ETL development, or data platform development. Strong hands-on development experience with:

• Python

• PySpark / Apache Spark

• Advanced SQL Experience with Machine Learning model development, training, and tuning. Experience working with large-scale datasets and performance optimization. Strong understanding of data concepts including data modeling, lineage, metadata, and governance. Experience supporting regulated environments with emphasis on controls and audit readiness. Strong communication skills with the ability to work effectively with both engineering and business partners.

Preferred Qualifications

• Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform. Experience with storage and compute technologies including S3, ADLS, GCS, Spark clusters, or Databricks. Experience with orchestration tools such as Airflow or similar scheduling platforms. Experience with streaming technologies such as Kafka. Experience with Databricks, Snowflake, or BigQuery. Experience with CI/CD, Git, automation, pipelines, and release management. Knowledge of data governance and security practices including encryption, access controls, data masking, and PII handling. Prior experience supporting Financial Crimes domains including AML, sanctions, fraud, investigations, or KYC.

Domain Experience

• Experience supporting AML, Transaction Monitoring, Investigations, Sanctions Screening, Fraud, or similar risk and compliance functions. Familiarity with regulatory expectations and strong documentation discipline.