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About This Role
Your role at Dynatrace
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The Lead Platform Engineer, Data \& AI is responsible for designing and developing solutions for the operational health, governance, and continuous improvement of the data platform, owning Snowflake administration, data pipeline/dbt administration, and the automation of the broader data technology stack from end to end. This is a hands\-on operational and engineering role. The right candidate combines deep platform administration skills with a systems\-thinking mindset: they identify manual processes and eliminate them through automation, maintain platform reliability at scale, and are beginning to explore how AI agent capabilities can extend the value delivered to data consumers.
Key Responsibilities:
- Snowflake Platform Administration: Own Snowflake administration end\-to\-end across development, staging, and production environments consolidating instances, covering warehouse sizing and cost governance, query optimization, object lifecycle management, RBAC design, Row\-Level Security policy implementation, and access provisioning automation. Maintain platform health through proactive monitoring, manage Snowflake feature adoption (Streams, Tasks, Dynamic Tables, Snowpipe), and act as the primary escalation point for Snowflake performance and access issues.
- dbt Administration \& Governance: Administer the dbt project and deployment infrastructure \- owning project configuration, model architecture standards, environment and job management, test coverage enforcement, documentation requirements, and model promotion workflows across environments. Monitor dbt run health and lineage, configure observability tooling (Elementary or equivalent), and partner with analytics engineers to review and certify models for production use.
- Data Stack Automation \& Operations: Automate the operational layer of the data technology stack \- including ETL/ELT tool administration (Fivetran, etc), RBAC and access provisioning via Terraform or scripting, alerting and notification pipelines for data quality and platform health, and CI/CD release workflows for dbt and platform configuration. Reduce manual operational toil by building reusable automation frameworks that make routine platform tasks fast, auditable, and self\-service where appropriate.
This is a remote eligible position. Candidates who sit within a 45 mile radius of Boston, MA; Denver, CO; Detroit, MI will be required to work hybrid (2 days per week in office). All candidates will be required to work EST hours.
What will help you succeed
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Minimum Requirements
- 12\+ years hands\-on data platform engineering
- 6\+ years of hands\-on data platform experience, with direct ownership of a Snowflake environment at production scale.
Preferred Requirements
- Deep Snowflake DBA skills: virtual warehouse sizing, auto\-suspend and scaling policy configuration, multi\-cluster warehouse management, and credit cost governance.
- Snowflake RBAC design: functional role hierarchy design, privilege grants, service account management, and systematic access provisioning \- not one\-off manual grants.
- Row\-Level Security implementation using Snowflake row access policies for multi\-tenant or restricted datasets.
- Snowflake feature ownership: Streams, Tasks, Dynamic Tables, Snowpipe, External Stages, Data Sharing, and Secure Views in production workloads.
- Query profiling and optimization: reading query profiles, identifying bottleneck operators, applying clustering keys, and resolving warehouse contention.
- Deep experience administering dbt project end\-to\-end \- model architecture (staging / intermediate / marts), incremental and snapshot patterns, and environment separation
- dbt Cloud or dbt Core deployment administration: job scheduling, environment variable management, run monitoring, failure alerting, and model promotion across dev/staging/prod.
- Test coverage governance: enforcing schema tests, data tests, and source freshness checks as mandatory gates before production promotion.
- dbt observability: configuring and maintaining Elementary, or equivalent tooling \- including artifact\-based monitoring, model health dashboards, and freshness tracking against Snowflake account usage
- Experience partnering with analytics engineers to review model design, optimize underperforming models, and maintain lineage and metadata for downstream governance.
- Platform\-level administration of Fivetran or Matillion: connector governance, sync scheduling, schema drift policy, user and environment management, and operational escalation handling.
- Alerting and notification automation: configuring and owning alerts for pipeline failures, data quality breaches, SLA violations, and Snowflake cost spikes \- routed to Slack, PagerDuty, email, or ServiceNow with actionable diagnostic context.
- CI/CD for the data platform: GitHub Actions or GitLab CI pipelines for dbt, Terraform, and ETL tool configuration with environment promotion gates, automated testing, and rollback on failure.
- Strong Python and SQL: used for automation scripting, operational tooling, and platform health monitoring, not just pipeline development.
- Hands\-on experience enabling Snowflake Cortex AI features, Cortex Analyst semantic model configuration, Cortex Search index setup, and production use of LLM functions
- Experience developing analytics agents on behalf of business stakeholders, connecting Snowflake data to Slack or other collaboration tools via bot APIs, webhooks, or slash commands so users can query data and receive insights in their workflow.
- Practical experience configuring or fine\-tuning LLMs (Cortex, OpenAI, Anthropic Claude, or Azure OpenAI) including prompt engineering, system instruction design, and parameter tuning to improve accuracy on governed data.
- Ability to design and maintain a semantic data layer, verified views, curated schemas, and structured metadata that grounds LLM\-generated outputs in accurate, governed Snowflake data.
- Snowflake SnowPro Core, SnowPro Advanced, or Cortex AI certification.
- Experience with multi\-agent frameworks (LangChain, LangGraph, or AutoGen) applied to enterprise data workflows.
Why you will love being a Dynatracer
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- A one\-product software company creating real value for the largest enterprises and millions of end customers globally, striving for a world where software works perfectly.
- Working with the latest technologies and at the forefront of innovation in tech on scale; but also, in other areas like marketing, design, or research.
- A team that thinks outside the box, welcomes unconventional ideas, and pushes boundaries.
- An environment that fosters innovation, enables creative collaboration, and allows you to grow.
- A globally unique and tailor\-made career development program recognizing your potential, promoting your strengths, and supporting you in achieving your career goals.
- A truly international mindset that is being shaped by the diverse personalities, expertise, and backgrounds of our global team.
- A relocation team that is eager to help you start your journey to a new country, always there to support and by your side.
- Attractive compensation packages and stock purchase options with numerous benefits and advantages.
Compensation and Rewards
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DOE, salary $140K \- $163K, plus Health, Dental, Life, STD, LTD, 401K, PTO. Total compensation may vary depending on candidate experience/education and location.
Equal Employment Opportunity
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All your information will be kept confidential according to EEO guidelines.
We offer competitive compensation, company\-sponsored premium benefits, medical, dental, vacation/holidays, company matching 401(k) Plan, etc. Dynatrace is an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to race, sex, color, gender identity, religion, national origin, ancestry, citizenship, physical abilities, age, sexual orientation, creed, disability status, veteran status, pregnancy, genetic status, or any other characteristic protected by law. If your disability makes it difficult for you to use this site, please contact [email protected] . Dynatrace participates in E\-Verify, participant information in English and Spanish. Right to work information in English and Spanish. EEO is the Law . To be considered for this position, please upload your resume/CV.
Note to Recruiters and Agencies : Thank you for your interest in Dynatrace. Please note that we do not accept unsolicited agency resumes —do not forward them via our website or directly to Dynatrace employees. Dynatrace will not pay fees for unsolicited resumes, and any resumes received this way will be considered the property of Dynatrace.
Benefits and work\-life perks
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We offer best\-in\-class core rewards, including paid time off, financial security benefits, retirement savings plans, and health insurance. Beyond that, you’ll get other benefits and work\-life perks designed to make your ride with us even more rewarding.
#### Mental health support
Our Employee Assistance Program, powered by Telus Health, offers support for you and your family members.
#### Wellness Days
Four company\-designated extra paid days off for you to recharge batteries.
#### Flexibility
Our hybrid working model and flexible working hours offer you the flexibility you need.
#### Employee Stock Purchase Plan
Purchase company stock ( NYSE:DT ) at a discounted price and become a shareholder.
#### Learn \& develop
Company\-wide learning perks, designated team's learning days, and more.
#### Volunteering day
A day of paid volunteer time to support a community or cause you care about.
#### Regular team events
We host Global Culture Parties, Family \& Friends at Work Day, Global Breakfasts, Green Weeks, Pride Month, and beyond!
#### International vibe
Most of our offices and teams are proudly multicultural. English is our shared language, but we embrace and learn from each other's cultures.
Rewards vary depending on your employment type. Some benefits and perks also differ by location — explore your city to see what’s available there.
About Dynatrace
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Dynatrace (NYSE: DT) is the leading AI\-powered observability and security platform. We're advancing observability for today's digital businesses, helping transform modern digital ecosystems' complexity into powerful business assets.
Our AI\-driven insights cut through the noise, allowing customers to focus on what truly matters by automating manual tasks and resolving issues with pinpoint accuracy. Dynatrace offers simplicity, clarity, and reliability at scale to ensure teams can make informed decisions, minimize downtime, and drive their business forward with confidence.
Salary Context
This $140K-$163K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Dynatrace, 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 $185,000 based on 13,200 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($151K) sits 18% below the category median. Disclosed range: $140K to $163K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Dynatrace AI Hiring
Dynatrace has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Boston, MA, US, Remote, US. Compensation range: $163K - $220K.
Location Context
AI roles in Boston pay a median of $216,350 across 460 tracked positions. That's 8% above the national 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 median, while Prompt Engineer roles sit at $140,000. 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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