Lead Data Platform Engineer

Tags: Christian Data Jobs • Remote Christian Jobs • Christian Data Engineer Jobs • Christian Snowflake Jobs • Christian AI Jobs • Christian Machine Learning Jobs • Christian Tableau Jobs • Christian Data Analytics Jobs • Christian Business Intelligence Jobs • Christian DevOps Jobs • Christian CI/CD Jobs • Christian MLOps Jobs Position Summary Virtuous is evolving its data platform into an AI-ready foundation that powers trusted decision-making and self-service analytics across the company. We’re hiring a Lead Data Platform Engineer to design, build, and own the systems that power our data ecosystem. This role is ideal for a highly motivated self-starter who is comfortable with ambiguity and thrives at the intersection of systems design, business semantics, and AI enablement. This is not a traditional data engineering role focused on building one-off pipelines, dashboards, or ad hoc reports. Success in this role comes from designing and building durable platform capabilities — security models, access patterns, cost controls, and shared data foundations — that enable teams ( and AI systems ) to safely and confidently use data at scale. You’ll work closely with the Director of Data Operations as well as our Finance, Product, Engineering and Security Teams to ensure our data platform is accurate, governable, and ready to support AI-enabled workflows across the company. What You'll Build & Own Data Platform & AI-Enablement Own the evolution of Virtuous’s data platform, primarily on Snowflake and dbt , as a secure, scalable, and AI-ready system. Design data models, metadata, and access patterns that support natural-language querying and AI-assisted analysis . Partner with Data Operations and Teams across the company to ensure data structures are accurate, reusable, and aligned with business definitions. Prepare data foundations that allow AI tools to deliver consistent, timely, and trustworthy answers. Ensure AI systems access data exclusively through governed service accounts and role-scoped permissions, with query activity auditable and restricted according to enterprise data classification and access standards. Access, Trust & Governance by Design Architect and implement role-based access controls within Snowflake and related data systems (including row- and column-level security), ensuring enforcement aligns with enterprise IAM standards, approved access policies, and centralized identity governance processes. In partnership with Security Operations and IT, translate approved enterprise access and compliance policies into enforceable platform-level controls, and maintain technical configurations to ensure ongoing alignment with those standards. Ensure all platform-level access controls integrate with the enterprise identity provider (SSO, SCIM, role lifecycle management), and support automated provisioning, deprovisioning, and periodic access certification processes. Build governance patterns that are enforced by design — not by manual process. Support periodic access reviews and control validation processes in coordination with Security and Compliance teams, ensuring appropriate separation of duties between policy definition, approval, and technical implementation. Platform Leverage & Standards Build and maintain CI/CD pipelines , testing strategies, and deployment patterns for dbt and Snowflake. Design deployment, testing, and validation patterns that make accuracy the default. Establish platform standards, templates, and best practices that enable others to move faster without sacrificing quality or security. Increase the amount of trusted work the organization can do without increasing Data Ops involvement. Reduce ad hoc work by building reusable systems and guardrails. Act as a technical leader and thought partner across data-related initiatives. Reliability, Cost & Performance Own Snowflake warehouse strategy, resource governance, and cost optimization. Continuously improve performance, scalability, and reliability across the data platform. Identify and eliminate inefficiencies that increase cost, risk, or operational overhead. Collaborate with IT and Cloud Engineering to ensure Snowflake networking, storage integrations, and data movement patterns align with enterprise cloud security baselines, network segmentation standards, and infrastructure governance policies. What Success Looks Like Teams reliably self-serve data and insights without increasing DataOps workload or risk. AI-powered tools consistently answer business questions accurately, using governed data, with access enforced by role and context. Data access, governance, and correctness are enforced by platform design rather than manual review or process. Platform improvements materially reduce cost, operational risk, or time-to-insight. Shared, trusted models replace bespoke datasets and one-off definitions. At least one company-critical initiative (e.g., AI enablement, cost optimization, access expansion) succeeds specifically because of syst