Senior Data & AI Platform Engineer

Workiva Inc.•Remote - IA, IA

19h•$111,000 - $178,000•Remote

About The Position

As a Senior Data and AI Platform Engineer at Workiva, you will be a key hands-on engineer driving the build, operation, security, and optimization of our Enterprise Data Platform. You will implement and maintain account-level infrastructure—including Snowflake warehouses, RBAC permission models, data replication, dbt Mesh frameworks, and automated platform guardrails. Working as part of the Data & Analytics (DnA) function under the CIO, you will translate architectural strategy into scalable, reliable platform components that enable domain teams to build and operate safely. In this role, you will help build and operate the data foundation for AI applications and agentic workflows, including Model Context Protocol (MCP) servers and evaluation pipelines. You will collaborate closely with Data Engineering, Analytics Engineering, AI/ML Platform, Security, and Governance (GRC) teams to implement robust data controls, enterprise cataloging (Atlan), and cost attribution frameworks.

Requirements

5+ years of relevant experience in data engineering or platform engineering, including 2+ years of hands-on experience managing or operating Snowflake platform infrastructure
Bachelor’s degree in Computer Science, Engineering, Math, Statistics, Finance, or a related discipline, or equivalent practical experience
Proven hands-on experience with SQL query tuning, Snowflake account administration/RBAC, and platform automation

Nice To Haves

Strong hands-on experience with dbt (Core or Cloud), dbt Mesh, and data mesh principles across enterprise domains
Hands-on experience with Snowflake RBAC, row/column masking, secure views, data cataloging tools (Atlan), and compliance standards (FedRAMP, SOX, or equivalent).
Familiarity or hands-on experience with Model Context Protocol (MCP), LLM data retrieval pipelines, vector/semantic search patterns, or Cortex
Strong scripting skills in Python for platform automation; working knowledge of AWS data infrastructure (S3, IAM, Secrets Manager)
Experience with Airflow, Fivetran, Workato, and enterprise BI tools (QuickSight, Sigma, Tableau, Omni)
Demonstrated experience analyzing query logs, tuning warehouse configurations, and optimizing cloud compute costs
SnowPro Core or Advanced certification
Strong written and verbal communication skills to partner effectively across engineering, security, and business analyst teams
Ability to work effectively in an Agile/Sprint environment, translating technical tasks into clean, well-documented deliverables
Track record of working cross-functionally and helping elevate team-wide engineering practices through peer mentoring and code reviews

Responsibilities

Build and maintain Snowflake platform infrastructure: Configure and operate warehouses, resource monitors, query tags, replication, account parameters, and advanced features (such as Iceberg, External Access Integration, and compute pools).
Implement data mesh boundaries: Build and support dbt Mesh patterns and domain boundaries across business functions (Finance, Marketing Ops, CPX, etc.).
Manage access & security models: Maintain RBAC permission models, service-user provisioning, solution-owner access patterns, and least-privilege enforcement in partnership with Okta and App Cafe.
Maintain core data stack integrations: Operate and optimize integration patterns for orchestration (Airflow), ingestion (Fivetran), operational tools (Workato, Salesforce), and ELT workflows within established guardrails.
Set and enforce operational standards: Drive best practices for naming conventions, schema/database layouts, environment promotion patterns, and code reviews across data teams.
Build AI-ready data infrastructure: Implement Snowflake data access patterns for LLM pipelines, semi-structured data consumption, context retrieval, and feature store integrations in partnership with AI/ML teams.
Operate MCP server infrastructure: Deploy and manage Model Context Protocol (MCP) servers that safely expose governed Snowflake data to AI agents and LLM applications.
Execute agent testing & evaluation: Maintain evaluation pipelines and test suites to validate agent accuracy, detect hallucination risks, and verify data domain coverage before production release.
Implement compliance & privacy controls: Partner with GRC and Security to execute FedRAMP boundary controls, field-level masking, data sanitization, and schema security reviews.
Cataloging & metadata management: Operate and integrate Atlan for enterprise data cataloging, column-level lineage, and lakehouse metadata governance.
Optimize performance & cost efficiency: Drive cost visibility using query tags, warehouse sizing optimizations, and showback alignment with business departments.
Support multi-model data consumption: Enable BI tools (QuickSight, Sigma, Tableau, etc.), analyst personas, and developer workflows through performance tuning and access guidance.
Peer mentorship & quality: Participate in design reviews, conduct pull request reviews, and mentor mid-level/junior engineers on data platform standards and dbt patterns.