Principal Data Engineer

Position Description:

Develops data pipelines that support next generation surveillance solutions on a wide range of anti-money laundering (AML) typologies, primarily supporting the team’s work in the money movement, trading, and cryptocurrency space. Collaborates across teams to understand the various regulatory (FINRA, OCC, and FinCEN) red flags and risks, and acquires data, performs data analysis, and builds new features to improve detection. Works with machine learning (ML) models, rewrites legacy models, and supports the data preparation for reporting efforts. Simplifies infrastructure and production support complexity. Develops model performance metrics and makes model adjustments accordingly to ensure models are performing optimally. Develops features using various technologies -- Data Build Tool (DBT), Snowflake, SQLMesh, and Python. Assists in the architectural design and implementation of the transaction monitoring platform that enables and scales the newest business initiatives, including digital assets and other emerging technologies. Creates robust testing scenarios, including unit testing, regression testing, and model performance.

Primary Responsibilities:

• Provides technical leadership, mentoring, and training to other team members through code reviews, collaboration, and educational presentations.
• Explores new technologies and emerging trends and determines their applicability to use cases and orchestrates the adoption of technologies and trends.
• Develops complex or multiple data engineering solutions and conducts studies of alternatives to translate divisional initiatives into business solutions.
• Analyzes and recommends changes in project development policies, procedures, standards, and strategies to development experts and management.
• Participates in architecture design teams.
• Defines and implements application-level architecture.
• Develops applications, components, and subsystems to support division-wide complex projects.
• Recommends development testing tools and methodologies.
• Establishes full project life cycle plans for complex projects across multiple platforms.
• Advises on risk assessment and risk management strategies for projects.
• Plans and coordinates project schedules and assignments for multiple projects.
• Provides technology solutions to daily issues and estimates technical evaluation requirements for technology initiatives.
• Mentors junior team members.

Education and Experience:

Bachelor’s degree in Computer Science, Engineering, Information Technology, Information Systems, or a closely related field (or foreign education equivalent) and five (5) years of experience as a Principal Data Engineer (or closely related occupation) developing data pipelines using Continuous Integration and Continuous Delivery (CI/CD) and deploying code and services to cloud-based data platforms -- DbT and Snowflake.

Or, alternatively, Master’s degree in Computer Science, Engineering, Information Technology, Information Systems, or a closely related field (or foreign education equivalent) and three (3) year of experience as a Principal Data Engineer (or closely related occupation) developing data pipelines using Continuous Integration and Continuous Delivery (CI/CD) and deploying code and services to cloud-based data platforms -- DbT and Snowflake.

Skills and Knowledge:

Candidate must also possess:

• Demonstrated Expertise (“DE”) developing high quality data solutions in a multi-developer Agile environment according to design and coding best practices; and developing Extract, Load, Transform (ELT) and Extract, Transform, Load (ETL) pipelines to migrate data to and from Snowflake data store, using DbT, Python, and SQLMesh.
• DE assembling large, complex data sets that meet functional and non-functional business requirements; designing and implementing internal process improvements -- automating manual processes, optimizing data delivery, and re-designing infrastructure for greater scalability, using Jira, Confluence, Tableau, and Actimize.
• DE designing, implementing, and maintaining scalable ETL/ELT pipelines, using tools (DbT and Snowflake); automating deployments and testing workflows, using Jenkins and GitHub; contributing to codebases using Git, conducting code reviews, and collaborating, using GitHub; writing unit and integration tests for data pipelines, using PyTest and DbT frameworks; and creating and maintaining technical documentation, runbooks, and onboarding guides.
• DE manipulating, processing, and extracting value from large, disparate datasets, using Alteryx, Snowflake, DbT, SQLMesh, and Python; working in financial crime typologies, AML/BSA regulations, or fraud detection methodologies.

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