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About This Role
Company Summary:
EchoStar is reimagining the future of connectivity. Our business reach spans satellite television service, live\-streaming and on\-demand programming, smart home installation services, mobile plans and products.
Today, our brands include Boost Mobile, DISH TV, Gen Mobile, Hughes and Sling TV.
Department Summary:
Our Technology teams challenge the status quo and reimagine capabilities across industries. Whether through research and development, technology innovation or solution engineering, our team members play a vital role in connecting consumers with the products and platforms of tomorrow.
Job Duties and Responsibilities:
Candidates must be willing to participate in at least one in\-person interview, which may include a live whiteboarding or technical assessment session.
The Data \& AI Governance engineer bridges the gap between high\-level data governance policies and actual code execution. Working directly within Line of Business (LOB) delivery pods, you will act as the working\-level governance partner. You are responsible for the technical implementation of data and AI governance, specifically leveraging but not limited to the Databricks (Unity Catalog) \& Snowflake (Horizon Catalog) ecosystems to ensure the delivery of high\-quality, safe, and compliant data products.
Rather than acting as a traditional auditor, you will "shift left" by integrating automated quality gates and lineage mapping directly into the development lifecycle, serving as the first line of defense for data and AI model safety. What Success Looks Like:* Data \& AI Governance Implementation: Partner with cross\-functional teams to implement Data \& AI Governance policies and standards as an overarching Governance Layer on Databricks / Snowflake \& other Data Platforms
- Embedded Pod Integration: Serve as the dedicated governance and technical data quality resource within agile delivery pods. Actively participate in stand\-ups, sprint planning, and retrospectives to detect compliance and quality risks early, preventing deployment bottlenecks
- Rapid Risk Assessment \& Triage: Manage the initial intake and classification of new data and AI use cases. Perform rapid risk assessments and "T\-shirt sizing" to determine the appropriate level of scrutiny, ensuring low\-risk initiatives move to production at high velocity while high\-risk models receive robust validation
- Consultative Guidance \& Liaison: Translate complex corporate governance requirements, data classification rules, and testing evidence mandates into clear, actionable technical instructions for delivery teams. Bridge the communication gap between business stakeholders and technical engineering pods
- Continuous Process Optimization: Monitor the practical performance of governance controls within active pods. Partner with leadership to identify friction points, reduce administrative overhead, and continuously automate the "Governance Engine" to accelerate delivery cycles
- Automation \& Monitoring:Own the build\-out of automated governance monitoring and observability — including statistical distribution, variance, and drift checks across Bronze/Silver/Gold data layers — leveraging SQL/Python engineering to deliver scalable, repeatable quality assurance at the platform level
Skills, Experience and Requirements:
Core Skills and Competencies (What You'll Bring)* Coding \& Automation: Python, Infrastructure as Code (Terraform), and workflow orchestration tools
- Data \& Systems: Cloud platforms, data catalogs, lineage tracking, and access management
- Hands\-on experience managing governance features in Databricks \& Snowflake for cataloging, fine\-grained access control, and end\-to\-end lineage tracking) and Databricks Genie
- Strong understanding of Lakehouse architectures, Delta Lake, Glue and Horizon Catalog as well as data pipelines to inspect, and validate underlying data quality.
- Proven ability to translate high\-level compliance policies (e.g., data privacy, security classifications) into concrete technical designs and configuration rules
- Modern Governance Principles: Strong understanding of "shift\-left" governance \- integrating quality, compliance, and risk checks directly into the early stages of the software/data development lifecycle rather than treating them as a final gate
- Comfort operating in fast\-paced, pod\-based agile environments (Sprints, Stand\-ups, Retrospectives) while maintaining an uncompromising focus on "Quality Gates"
- Strong analytical skills to evaluate data/AI use cases, distinguish between varying risk levels in real\-time, and apply proportional governance controls
Minimum Requirements
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- Minimum Education: Bachelor’s Degree in Computer Science, Data Engineering, Information Systems, or a related technical field
- Minimum Experience: 2\-4 years of experience in Data Engineering, Data Analyst, Product Management or Technical Consulting with an emphasis on data management or governance
- Required Technical Skills: Must have at least 2 years of experience with:
+ Databricks (Unity Catalog) and/or Snowflake (Horizon Catalog) governance administration
+ SQL, Python, Infrastructure as Code (Terraform), and workflow orchestration tools.
+ Implementation of "shift\-left" automated data quality, privacy, and risk governance controls
Benefits:
We offer versatile health perks, including flexible spending accounts, HSA, a 401(k) Plan with company match, ESPP, career opportunities, and a flexible time away plan; all benefits can be viewed here: EchoStar Benefits.
The base pay range shown is a guideline. Individual total compensation will vary based on factors such as qualifications, skill level, and competencies; compensation is based on the role's location and is subject to change based on work location.
Candidates need to successfully complete a pre\-employment screen, which may include a drug test and DMV check. Our company is committed to fostering an inclusive and equitable workplace where every individual has the opportunity to succeed. We are dedicated to providing individuals with criminal or arrest records a fair chance of employment in accordance with local, state, and federal laws.
The posting will be active for a minimum of 3 days. The active posting will continue to extend by 3 days until the position is filled.
We pride ourselves on developing and promoting talent as an Equal Employment Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or protected veteran status. EchoStar will accommodate the sincerely held religious beliefs of employees if such accommodations are not undue hardships and are otherwise within the bounds of applicable law. All qualified applicants with arrest or conviction records will be considered for employment in accordance with local, state, and federal law. You may redact any information that identifies age, date of birth, or dates of school/graduation from your application documents before submission and throughout our application process.
EchoStar will provide reasonable accommodation to otherwise qualified job applicants and employees with known physical or mental disabilities, unless doing so poses an undue hardship on the Company, poses a direct threat of substantial harm to others, or is otherwise not required by law. EchoStar has a more detailed Accommodation Policy that applies to employees. EchoStar endeavors to make echostar.com and jobs.echostar.com accessible to users. Please contact [email protected] if you would like to discuss the accessibility of our website or need assistance completing the application process. This contact information is for accommodation requests only; do not use this contact information to inquire about the status of applications.
Click the links to access the following statements: EEO Policy Statement, Pay Transparency, EEOC Know Your Rights (English/Spanish)
Salary Range: USD $96250\.00 \- $137500\.00 / Year
Salary Context
This $96K-$137K range is in the lower quartile 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 EchoStar, 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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($116K) sits 46% below the category median. Disclosed range: $96K to $137K.
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.
EchoStar AI Hiring
EchoStar has 7 open AI roles right now. They're hiring across Data Engineer, AI/ML Engineer, Data Scientist, AI Product Manager. Positions span Denver, CO, US, Englewood, CO, US, San Mateo, CA, US. Compensation range: $118K - $208K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 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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