Data Engineer – AML & Financial Crime Analytics

Location: Remote
Department: Engineering / Data Team
Reports to: Head of Engineering or Director of Data
Job Type: Full-Time
Overview
We are seeking a Data Engineer to join our analytics and data science team focused on building and maintaining high-quality data pipelines across multiple data sources. The ideal candidate has experience working with file-based data exports from a variety of banking systems—such as Jack Henry, Fiserv, FIS, Finastra, and CSI—as representative examples of common financial data sources.
This role centers around designing scalable pipelines, ensuring data quality, and transforming heterogeneous financial feeds into a consistent internal schema that supports AML, fraud analytics, and regulatory reporting.
Key

Responsibilities
Design & Develop Pipelines: Build and maintain scalable ETL/ELT pipelines to ingest, normalize, and transform data from structured and semi-structured formats (CSV, XML, JSON, SFTP drops, etc.).
Data Standardization: Translate diverse data feeds—including those exported from banking systems—into a unified schema used across AML, fraud, and customer risk analysis.
Data Quality & Validation: Develop robust validation logic and monitoring tools to detect anomalies and ensure data integrity across ingestion layers.
Vendor-Agnostic Integration: Map and ingest data from a wide variety of banking software vendors (e.g., Jack Henry, Fiserv, FIS, Finastra, CSI), with the understanding that these are common industry examples—not requirements.
Cross-Team Collaboration: Work with data scientists, product managers, and compliance experts to meet evolving analytical and regulatory needs.
Serve as a technical liaison with clients, helping them structure their data exports, diagnose issues with feed delivery or formatting
Pipeline Optimization: Tune pipeline performance, storage efficiency, and scalability across batch and streaming environments.
Documentation & Compliance: Maintain clear, auditable documentation for data source mappings, transformations, and quality rules.
Security First: Prioritize secure handling of sensitive financial and personally identifiable information using industry best practices.
Required

Qualifications
3+ years of experience as a Data Engineer or similar role working with large-scale data transformation pipelines.
Strong proficiency in Python and SQL.
Familiarity with ingesting file-based data formats (e.g., fixed-width, CSV, XML, JSON).
Experience integrating data from banking systems or other financial applications (specific vendors are not required).
Understanding of data modeling, schema design, and schema harmonization across diverse data feeds.
Hands-on experience with data pipeline orchestration tools such as Airflow, dbt, Dagster, or similar.
Experience with cloud platforms like GCP and/or AWS (S3, Lambda, Glue, RDS, etc.).
A strong focus on data quality, integrity, and traceability in regulated environments.
Bonus Experience
Exposure to AML or fraud analytics pipelines.
Experience working closely with data scientists or ML engineers.
Familiarity with streaming data systems like Kafka or Kinesis.
Previous work with financial institutions, fintechs, or regtech platforms.
Experience with data warehousing tools like BigQuery, Redshift, or Snowflake.
Why Join RiskScout?
Play a critical role in building infrastructure that supports real-time fraud detection and financial crime prevention.
Collaborate with a driven, talented team in a mission-focused startup environment.
Enjoy remote work flexibility with a culture of ownership, transparency, and meaningful technical work.
Solve novel data challenges at the intersection of compliance, regulation, and technology.
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