Sr. Data Platform Engineer

Docusign is a leading company in e-signature and contract lifecycle management, serving over 1.5 million customers worldwide. The Data Platform Engineer will design, build, and operate a next-generation data and AI platform, focusing on high-quality analytics and AI/ML capabilities while collaborating with various stakeholders to deliver actionable insights.

Responsibilities:

• Design, build, and maintain scalable, secure, high‑performing data and AI platforms using Snowflake and AI infrastructure components (e.g., feature stores, model registries, model serving endpoints)
• Own and optimize the Snowflake environment (warehouses, databases, schemas, roles, resource monitors) with a focus on performance tuning, cost optimization, and capacity planning for both data and AI workloads
• Stay current on Snowflake releases, the modern data stack, BI tooling, AI/ML, MLOps, and generative AI best practices and proactively recommend platform improvements
• Design, build, and operationalize AI capabilities in Snowflake using Snowflake Cortex and native Snowflake AI features to power governed, production‑grade conversational, retrieval‑augmented, and predictive applications
• Build and operate AI agents and workflows in Snowflake Cortex or similar platforms, integrating tools and context while optimizing prompt patterns to ensure reliable, high‑quality LLM outcomes
• Contribute to and evolve the overall data, ML, and AI architecture (warehouse, lake/lakehouse, streaming, feature and vector stores, model serving, AI app layers) while establishing and enforcing best practices for data modeling, AI/ML pipeline development, code reviews, testing, deployment, and documentation across the stack
• Automate infrastructure and deployment using CI/CD and Infrastructure‑as‑Code for data pipelines, ML workflows, and AI/LLM services
• Architect and manage AWS‑based data and AI infrastructure including S3‑backed data lakes, MWAA/Airflow environments, and supporting services for data ingestion, transformation, and model deployment
• Implement robust monitoring, logging, and cost‑management practices for AWS data and AI services to ensure platform reliability, security, and efficiency
• Collaborate with cloud, networking, security, compliance, and infrastructure teams to design resilient, scalable AWS architectures that integrate Snowflake, AI services, and downstream analytics/applications while implementing and maintaining strong data and AI governance (RBAC, masking, encryption, audit logging, responsible AI controls)
• Partner with data analysts, data scientists, ML/AI engineers, BI developers, and business stakeholders to translate data and AI requirements into scalable, secure technical solutions that deliver actionable insights and business value