• Data Infrastructure: Design, build, and optimize batch data pipelines, ELT processes, and Snowflake data warehouse objects to support efficient data ingestion, storage, and retrieval.
• dbt Development: Contribute to and extend the firm's dbt project, including seed management, model graph optimization, schema design, and pipeline reliability.
• Orchestration: Maintain and expand Dagster orchestration for batch ingestion pipelines, ensuring reliable scheduling and observability across daily, weekly, and monthly data loads.
• AWS Infrastructure: Build and maintain Snowflake-AWS integrations including S3 storage integrations, IAM roles, and CloudFormation configurations supporting our cloud data layer.
• Data Quality: Support data quality and reconciliation workflows for collateral data pledged against lending facilities. Identify inconsistencies and anomalies and implement appropriate resolutions.
• Data Modeling: Define and maintain data models and schemas that enable efficient analytics, reporting, and visualization, consistent with data governance standards.
• Documentation: Document data processes, pipeline flows, and data structures in a way that supports team knowledge-sharing and auditability.
• AI Agent Operations: Support the deployment, monitoring, and ongoing maintenance of AI agents developed by the data team, including managing data sources, pipeline dependencies, and integrations that those agents rely on.

• Bachelor's degree in Data Science/Analytics, Information Systems, Computer Science, or a related field.
• 2+ years of experience in a Data Engineering or Analytics Engineering role. Candidates with a strong analytics background and demonstrated pipeline development experience will also be considered.
• Experience working with finance-related data (FinTech, lending, financial services, or related industries preferred).
• Advanced SQL skills and demonstrated experience working with complex, multi-source data sets.
• Proficiency in Python for data pipeline development. Pandas required; Polars or PySpark a plus.
• Hands-on experience with core data engineering tools, including:

• Snowflake (data warehousing)
• dbt (data transformation and modeling)
• Dagster or Airflow (pipeline orchestration)
• AWS (S3, IAM, CloudFormation, or related services)
• REST API integration (data ingestion, outbound data delivery, and API-driven orchestration)

• Solid understanding of data modeling, ETL/ELT design patterns, and data governance principles.
• Strong attention to detail and a commitment to accuracy. In our environment, data quality has direct financial implications.
• Effective communication skills with both technical and non-technical stakeholders, including Credit and Operations teams who rely directly on the data this role supports.
• Willingness to stay current with emerging tools and practices, and ability to adapt to evolving business requirements in a fast-paced environment.
• Familiarity with AI/ML tooling or LLM-based pipeline development is a plus, including any of the following:

• Snowflake Cortex (AI functions, Cortex Analyst, or Snowflake Intelligence)
• AWS Bedrock (LLM model access and deployment via AWS infrastructure)
• Anthropic Claude API (prompt engineering, API integration, or embedding LLM calls into data workflows)

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