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About This Role
DataBank Holdings Ltd. is a leading provider of enterprise-class data center, cloud, and interconnection services, offering customers 100% uptime availability of data, applications, and infrastructure. DataBank's managed data center services are anchored in world-class facilities. Our customized technology solutions are designed to help customers effectively manage risk, improve technology performance, and allow focus on core business objectives. DataBank is headquartered in the historic former Federal Reserve Bank Building, in downtown Dallas, TX.
DataBank is proud to be an Equal Opportunity Employer. Our work culture does not discriminate based on actual or perceived race, creed, color, religion, alienage or national origin, ancestry, citizenship status, age, disability or handicap, sex, marital status, veterans' status, gender, gender identity, gender expression, genetic information, sexual orientation, or any other characteristic protected by applicable federal, state, or local law.
We're looking for a hands-on Director of AI & Data Analytics who still loves getting their hands dirty in code. This isn't a "pure management" role where you sit in meetings all day – You’ll spend roughly 50% of your time leading and mentoring your team and other engineering members, and the remaining 50% hands-on, writing SQL, building data pipelines, architecting solutions, and tackling complex technical challenges directly.
You'll be the technical architect and strategic driver of DataBank's data and AI initiatives, turning our mountain of infrastructure, operations, and customer data into actual intelligence that people use. Think: predictive analytics that prevent problems before they happen, AI models that optimize efficiency across data centers, and self-service analytics that make everyone's life easier. The ideal candidate blends technical depth in data architecture, AI/ML engineering, and cloud analytics with a strong business sense for driving measurable outcomes.
Key Responsibilities:
Be the Data Whisperer: Building Business Intelligence & Insights
- Partner with leaders across the business to understand their data challenges (and translate "I need a report" into "here's the insight you actually need")
- Design and model data solutions that enable self-service analytics – because teaching people to fish is way better than becoming the company's fish-delivery service
- Build cross-functional relationships with engineering, product, finance, operations, and marketing teams who all have "just one quick data question" teams to quantify ROI and inform strategic decision-making
- Measure and report key business metrics, operational efficiency, customer growth, and service quality
- Mentor and align business analysts in other departments on their reporting strategies
AI & Analytics Strategy
- Define and execute a company-wide AI and analytics roadmap aligned with DataBank’s business and technology objectives
- Drive the evolution from traditional BI to predictive and generative AI-driven insights across DataBank’s data ecosystem
- Champion the integration of AI into key workflows, from customer intelligence to operational automation.
- Develop AI models for forecasting, anomaly detection, and intelligent automation in data center operations
- Build ML pipelines that go beyond PowerPoints demos and into production
Fix What's Broken, Build What's Missing
- Untangle and optimize data structures that have gotten "organically complex" over time
- Optimize the existing MSSQL to Snowflake replication and data warehousing strategy to establish patterns of anti-fragility
Lead (When You're Not Coding)
- Build and mentor a high-performing team of data engineers, scientists, and AI specialists
- Define and execute an AI and analytics roadmap that aligns with business objectives (the real ones, not just buzzword bingo)
- Champion the evolution from "here's what happened" to "here's what's about to happen and what we should do about it"
Make Data Governance Not Boring
- Implement data quality, security, and compliance frameworks that meet regulatory standards
- Ensure AI practices align with compliance requirements where applicable
Qualifications:
Must-Haves:
- Bachelor’s degree in computer science, Data Science, Engineering, or related field (Master’s preferred)
- 10+ years wrangling data, building pipelines, or developing AI/ML solutions with 3+ years in leadership roles
- Proven experience building scalable data platforms and deploying production-grade AI/ML solutions.
- Deep expertise in data modeling – you dream in star schemas and know when to denormalize. Extra credit if you have a small shrine dedicated to Ralph Kimball.
- Proven track record building scalable data platforms and deploying production-grade AI/ML solutions
- Experience with modern AI/ML frameworks (PyTorch, TensorFlow, Hugging Face, LangChain)
- Deep Expertise in cloud-native data technologies, you've built data transformations with:
- Data warehousing - 5 years. Snowflake preferred, Databricks, AWS/Azure data services are an acceptable alternative
- Enterprise BI Reporting – 5 years. Sigma Computing preferred. Power BI or Tableau is an acceptable alternative
- Orchestration tools (Airflow and dbt)
What Makes This Role Interesting
This isn't just "build dashboards and call it analytics." You'll be:
- Designing the data foundation for one of the fastest-growing data center companies in the world, operating 65+ data centers across North America
- Building AI capabilities that optimize real-world physical infrastructure (power, cooling, capacity planning)
- Creating intelligence systems that make our customers' lives better and our operations more efficient
- Working cross-functionally with smart people who actually care about using data well
- Having the autonomy to fix technical debt while building new capabilities
*Translation: You get to solve hard technical problems, work with interesting data, and see real business impact from your work.*
Why Join?
- Lead a high-impact AI and data analytics function at the intersection of cloud, edge, and infrastructure innovation
- Collaborative, values-driven culture that prioritizes integrity, innovation, and operational excellence
- Competitive compensation, 401(k) match, health and wellness benefits, and generous PTO
To be considered for this position, all candidates must be U.S. citizens or hold valid Green Card and live in the U.S.. This requirement is due to DataBank’s compliance with FedRAMP and FISMA regulations.
No third-party recruiters.
#### Position Information
Company: DataBank Holdings
Position: Director of AI & Data Analytics
Status: Full Time
Shift: First (Day)
Req #: 10796676
Date Posted: October 23, 2025
Location: 400 S Akard Street, Dallas, US, TX, 75202
Job Category: Corporate
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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At DataBank, 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 $154,000 based on 8,743 positions with disclosed compensation. Director-level AI roles across all categories have a median of $230,600.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
DataBank AI Hiring
DataBank has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Dallas, TX, US.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: New York (1,633 roles, $204,100 median); Los Angeles (1,356 roles, $179,440 median); San Francisco (1,230 roles, $240,000 median).
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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $145,600. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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