Senior Data Engineer

📋 Description

• • Design, build, and scale enterprise-grade data pipelines on Snowflake to support high-volume transactional data processing for over 110M annual transactions and $257B in annual volume.
• • Optimize the foundational Snowflake architecture to improve performance, cost-efficiency, and data reliability across the organization’s analytics ecosystem.
• • Develop and deploy intelligent semantic models that directly feed into Versapay’s product ecosystem to enable data-driven decision-making and commercial data products.
• • Lead the evolution of the data platform from foundational governance to a robust, scalable infrastructure that supports agentic engineering and machine learning operations (MLOps).
• • Collaborate as a full-stack generalist within the engineering pod to ensure end-to-end ownership of pipeline resilience, data quality, and observability across all data workflows.
• • Implement advanced observability tooling to monitor data pipeline health, detect anomalies, and ensure SLAs for data availability and latency are consistently met.
• • Partner with analytics, product, and finance teams to translate business requirements into technical data solutions that accelerate AI enablement and unlock working capital insights.
• • Automate data ingestion, transformation, and enrichment processes from diverse B2B payment sources to ensure seamless integration across finance systems and customer platforms.
• • Establish and enforce data governance standards, including lineage tracking, access controls, and metadata management, to ensure compliance and data trustworthiness.
• • Drive innovation in data engineering practices by evaluating and integrating emerging tools and techniques for scalable, secure, and efficient data processing.
• • Mentor junior engineers and contribute to team-wide knowledge sharing on best practices in data pipeline design, cloud architecture, and MLOps.
• • Participate in cross-functional initiatives to align data platform strategy with company-wide goals around cash flow clarity, automation, and growth acceleration.
• • Maintain and enhance data models that support real-time and batch analytics used by finance leaders to drive business outcomes.
• • Ensure data pipelines are fault-tolerant, recoverable, and capable of handling peak transaction loads without degradation in performance or accuracy.
• • Document architectural decisions, data flows, and operational procedures to enable seamless onboarding and knowledge transfer across teams.
• • Contribute to the design and implementation of data products that generate high-margin revenue through enhanced customer insights and automation capabilities.