Staff Data and AI Platform Engineer

About this role

The Staff Data and AI Platform Engineer at Workiva serves as the technical authority for the Enterprise data platform. You'll own design, reliability, security, and cost efficiency of account-level infrastructure (warehouses, RBAC, replication, governance, platform standards) while enabling domain teams to build and operate dbt Mesh projects safely at scale. You'll set technical direction, translate ambiguous challenges into clear standards and architectural decisions, and raise the engineering bar across data and analytics.

You'll proactively evaluate emerging technologies (including AI/ML data substrate integration), shape the multi-year data platform roadmap, and drive buy/build/adopt decisions with leadership. Key partnerships include GRC on FedRAMP and data-boundary controls, Atlan for enterprise cataloging, and AI/ML platform teams on AI application foundations. You'll align stakeholders across Data Engineering, Analytics Engineering, Data Science, ML Platform, AI Product, BI, Security, Data Ops, and business partners—influencing technical direction without direct authority.

Reports to: Sr Director of Enterprise Data Platform (Data & Analytics function under CIO)

What You'll Do Platform Strategy & Technical Leadership • Define multi-year data platform architecture vision and roadmap; present tradeoffs and sequencing to DnA and engineering leadership

• Serve as technical decision-maker for platform-wide architectural choices: buy/build/adopt, technology evaluation, and cross-domain standard-setting

• Evaluate and pilot emerging data platform technologies; run POCs and develop architectural recommendations

• Drive alignment across Data Engineering, Analytics Engineering, AI/ML Platform, Security, and Data Ops

• Mentor data and analytics engineers; define engineering standards, review designs/PRs, and grow platform competency

Platform Engineering & Operations • Define and maintain Snowflake platform standards: naming conventions, schema/database layout, warehouse tiers, role hierarchy, environment promotion patterns

• Own RBAC permission model: analyst/engineer roles, service-user provisioning, solution-owner access patterns, least-privilege via Okta and App Cafe

• Design and evolve dbt Mesh and data mesh boundaries across business domains (Finance, Marketing Ops, CPX, others)

• Configure and operate Snowflake account infrastructure: warehouses, resource monitors, query tags, replication, account parameters, Iceberg, External Access Integration, compute pools

• Own integration with Atlan for enterprise data cataloging, lineage, and metadata lakehouse governance

• Define integration standards for orchestration (Airflow), ingestion (Fivetran), data sharing, and ELT tooling with guardrails for domain teams

AI & Agentic Data Infrastructure • Design Snowflake data architecture patterns for AI/agentic workflows: structured/semi-structured data access for LLM pipelines, context retrieval, feature store integrations, Snowflake Cortex or external model frameworks

• Build and operate MCP (Model Context Protocol) server infrastructure exposing Snowflake data to AI agents/LLM workflows, defining access patterns, routing logic, and guardrails

• Own analytical agent evaluation framework: tooling, standards, and automated testing for agent accuracy, hallucination risk, and coverage across governed data domains

Security, Governance & Cost • Partner with GRC and Security on FedRAMP boundary controls, data sanitization, field-level masking, and security reviews for new schemas/integrations

• Drive operational discipline via query-tag attribution, warehouse sizing strategy, and showback alignment with business departments

Enablement & Ecosystem • Enable multi-model data consumption (BI, business/AI applications, analysts, developers) through Snowflake connectivity, performance tuning, and access patterns within guardrails

• Define standards for reverse-ETL and operational workloads (Salesforce, OpenAir, Workato, Fivetran, similar); delegate execution to domain teams within guardrails

What You'll Need Minimum Qualifications • Bachelor's degree in Computer Science, Engineering, Math, Finance, Statistics, or related discipline (or equivalent practical experience)

• 8+ years in data or platform engineering, including 3+ years owning Snowflake account/platform operations at enterprise scale

• Demonstrated architectural judgment balancing standardization, domain autonomy, cost, security, performance, and buy/build/adopt decisions at enterprise scale

Preferred Qualifications Technical • RBAC model design, service-user provisioning, SSO/Okta integration for Snowflake

• Semantic layer design on Snowflake (semantic views, verified queries, metadata for human/AI consumers)

• MCP server or equivalent AI data gateway infrastructure for LLM-powered workflows

• Analytical agent evaluation frameworks: accuracy testing, hallucination detection, coverage validation

• Data governance: row/column masking, secure views, data cataloging (Atlan), compliance-boundary design (FedRAMP, SOX)

• Advanced SQL query design and tuning for performance, cost, and accuracy in Snowflake

• AWS data services (S3 staging, IAM, Secrets Manager) supporting Snowflake workloads

• Python or scripting for platform automation and provisioning

• dbt (Core or Cloud) and dbt Mesh or multi-project data mesh patterns

• BI/analytics tools (QuickSight, Omni, Sigma, Tableau); experience evaluating or migrating BI tooling

• Orchestration/ingestion tools (Airflow, Fivetran, Workato, or similar)

• Performance and cost optimization: warehouse tuning, query analysis, resource monitors, query tags

• Agile/Sprint environment experience

• SnowPro Core, Advanced, or Architect certification (preferred)

Leadership • Excellent verbal/written communication; ability to translate platform strategy for business, technical, and executive audiences

• Proven ability to influence technical direction and drive alignment across teams witho

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