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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 without direct authority
- Strong planning and prioritization to manage strategic roadmap, operational work, and emerging tech evaluations concurrently
- Comfort navigating ambiguity, defining structure, and driving decisions with incomplete information under organizational complexity
- Experience leading or contributing to cross\-functional initiatives
- SaaS or subscription\-based business experience
- Track record scaling enterprise data platforms across multiple business domains
Working Conditions
- Less than 10% travel
- Reliable internet access for remote working opportunities
\&\#xa;Workiva will not provide visa sponsorship for this position. Candidates must be authorized to work in the U.S. on a permanent basis.\&\#xa;
How You’ll Be Rewarded
✅ Salary range in the US: $129,000\.00 \- $210,000\.00
✅ A discretionary bonus typically paid annually
✅ Restricted Stock Units granted at time of hire
✅ 401(k) match and comprehensive employee benefits package
The salary range represents the low and high end of the salary range for this job in the US. Minimums and maximums may vary based on location. The actual salary offer will carefully consider a wide range of factors, including your skills, qualifications, experience and other relevant factors.
Why Join Workiva
Workiva is the platform designed to bring confidence, control, and a competitive edge to the world’s most complex organizations. Our AI\-powered platform unifies finance, risk, and sustainability on a single, secure foundation—ensuring data is trusted, traceable, and ready to act on. With an unbroken path from source to output, leaders gain confidence in their numbers, visibility into current and emerging risks, and the ability to move with speed and precision in a constantly changing world.
At Workiva, you’ll bring technology to market that executives, boards, and regulators depend on. The work you do here helps organizations navigate uncertainty, maintain trust, and make decisions that stand up to scrutiny. If you’re energized by meaningful challenges, inspired by collaborative teams, and motivated to help organizations turn uncertainty into advantage, we’d love to meet you.
Employment decisions are made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other protected characteristic.
Workiva is committed to working with and providing reasonable accommodations to applicants with disabilities. To request assistance with the application process, please email [email protected] .
Workiva employees are required to undergo comprehensive security and privacy training tailored to their roles, ensuring adherence to company policies and regulatory standards.
*Workiva supports employees in working where they work best \- either from an office or remotely from any location within their country of employment.*
\#LI\-MJ2
Salary Context
This $129K-$210K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Workiva, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($169K) sits 21% below the category median. Disclosed range: $129K to $210K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Workiva AI Hiring
Workiva has 4 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $194K - $308K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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