Data Engineer (Python / PySpark) - only w2

Role Summary:

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.

Key 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.

Required Skills:

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 (Google Cloud Platform)

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.