The Analytics team is evolving our enterprise capabilities from foundational governance into a robust data platform, safely accelerating strategic AI enablement and delivering high-margin commercial data products. As a Senior Data Engineer, you will be pivotal in optimizing and scaling our foundational Snowflake architecture while aggressively pushing toward agentic engineering and machine learning operations. You will operate as a full-stack generalist within the engineering pod, sharing cross-functional responsibility for pipeline resilience, advanced observability, and the deployment of intelligent semantic models that directly feed our product ecosystem.
Reports To: Manager of Data Engineering
What You'll Do:
Architect for the Future: Optimize our existing Snowflake architecture, establishing strict environmental isolation and scalable structures that prepare our data for eventual downstream commercialization and product offerings.
Drive Agentic Engineering: Leverage tools like Snowflake Cortex, Cursor, and UiPath to automate workflows, build semantic models, and deploy agents that accelerate time-to-value.
Establish Data Observability: Implement and manage robust data quality and observability frameworks to ensure pipeline reliability and proactive issue resolution.
Operationalize Machine Learning: Design and maintain MLOps pipelines to support the seamless rollout, monitoring, and lifecycle management of ML models directly within Snowflake.
Execute Shared Ownership: 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.
Model for Enterprise Utility: Synthesize disparate operational entities into a unified, enterprise-wide semantic model that supports both internal analytics and future data monetization efforts.
Qualifications
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).
What You'll Bring To The Team:
- Technical Competency: Advanced SQL skills, proficiency with Python/R, and experience with BI tools. Focus on self-sufficiency and leveraging AI tools to accelerate development.
- "Builder" Mentality: An ability to thrive in fast-paced environments with a track record of defining and executing high-impact initiatives. A desire to solve complex problems, remediate technical debt, and find creative solutions for scaling our platform.
- Business Acumen: Strong business acumen with a proven ability to translate complex data analysis into strategic recommendations. Adept at identifying key drivers and influencing decision-making. You understand the business behind the data and the path to commercialization.
- Empathetic Collaboration: Assertive with humility – able to communicate both persuasively and positively. Maintain high standards for verbal and written communication while seamlessly sharing domain responsibilities across the engineering pod.
- Trusted Advisor: Possesses a high degree of integrity, the relentless pursuit of truth, and an ability to inspire change, particularly in championing data quality and observability standards.
What Will Make You Stand Out:
Deep domain expertise navigating complex merchant payment ecosystems (e.g., Adyen), operating under rigorous enterprise data governance and security standards.
Proven ability to architect the translation of high-velocity transactional events into highly optimized, columnar analytical architectures.
Direct experience architecting data products for commercialization, external endpoints, or embedded analytics within a SaaS platform.
