Data & Analytics Engineer building scalable data platforms for Ledgebrook, an insurance company. Designing pipelines, governance, and cloud data systems while partnering with technical and insurance teams.
About the role
- Senior Data Engineer at Versapay optimizing Snowflake architecture for automating accounts receivable processes. Involved in AI enablement and delivering high-margin commercial data products.
Responsibilities
- Optimize our existing Snowflake architecture, establishing strict environmental isolation and scalable structures that prepare our data for eventual downstream commercialization and product offerings.
- Leverage tools like Snowflake Cortex, Cursor, and UiPath to automate workflows, build semantic models, and deploy agents that accelerate time-to-value.
- Implement and manage robust data quality and observability frameworks to ensure pipeline reliability and proactive issue resolution.
- Design and maintain MLOps pipelines to support the seamless rollout, monitoring, and lifecycle management of ML models directly within Snowflake.
- Partner closely with your peers under the Data Engineering Manager to share responsibilities across pipeline management, MLOps, and architecture, avoiding siloed knowledge and ensuring comprehensive team coverage.
- Synthesize disparate operational entities into a unified, enterprise-wide semantic model that supports both internal analytics and future data monetization efforts.
Requirements
- 5+ years of Data Engineering experience with a deep, specialized focus on Snowflake's advanced features (e.g., RBAC, materialized views, dynamic tables, Snowpipe, stored procedures).
- Advanced proficiency in SQL and Python, with a strong foundation in applying software engineering best practices to ELT processes.
- Observability Expertise: Hands-on experience implementing data observability and monitoring platforms (such as DataDog) to manage data quality at scale.
- AI & MLOps Exposure: Demonstrated experience using AI-assisted development tools (e.g., Cursor, Cortex) and familiarity with MLOps principles for productionalizing machine learning models.
- Pipeline Management: Experience building and maintaining resilient, low-touch data pipelines using modern integration and orchestration tools (e.g., Fivetran, AWS Glue, AWS Lambda).
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