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About This Role
Position Summary
Rosnet is looking for an experienced AI Data Engineer to sit at the intersection of data engineering, machine learning infrastructure, and AI\-driven product development. In this role, you will design, build, and maintain scalable data pipelines and AI/ML workflows that power Rosnet’s analytics products and operational intelligence capabilities. You will collaborate closely with the Data, Product, and Engineering teams to deliver reliable, high\-quality data solutions to embed AI capabilities directly into the product and internal workflows. This is an individual contributor role for someone who brings deep technical expertise and thrives in an environment where their work has direct, visible impact on the business and the clients we serve.
Key Responsibilities
- Data Engineering \& Pipeline Development
- Design, build, and maintain robust ETL pipelines that ingest, transform, and serve data across Rosnet’s platform.
- Develop and manage data models, schemas, and warehousing structures that support reporting and analytical workloads.
- Ensure data quality, integrity, and observability through monitoring, testing, and documentation practices.
- Optimize pipeline performance and cost efficiency across cloud\-based data infrastructure.
AI \& Machine Learning Integration
- Build and operationalize AI/ML models and workflows, including LLM\-based features, predictive analytics, and intelligent automation.
- Develop and maintain MLOps infrastructure including model training pipelines, versioning, deployment, and monitoring.
- Evaluate and integrate third\-party AI tools and APIs (including LLMs and generative AI platforms) into data and product workflows.
- Partner with Product and Engineering teams to translate business needs into AI\-powered features and data products.
Collaboration \& Technical Leadership
- Work cross\-functionally with Product, Engineering, and Client Services to define data requirements and deliver solutions.
- Contribute to architectural decisions and technical standards within the Data team.
- Document systems, pipelines, and processes clearly to support team knowledge\-sharing and operational continuity.
- Mentor and support junior data team members as the team grows.
Required Qualifications
Education
- Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, or a related field—or equivalent professional experience.
Experience
- 5–10 years of professional experience in data engineering, with at least 2 years involving AI/ML pipeline development or MLOps.
- Demonstrated experience building production\-grade data pipelines and working in cloud\-based data environments.
- Experience in the SaaS industry strongly preferred; restaurant, hospitality, or foodservice industry experience is a plus.
Technical Skills
- Proficiency in Python (or similar) for data engineering and ML model development.
- Strong SQL skills including complex query writing, data modeling, and performance tuning.
- Hands\-on experience with cloud data platforms (Azure preferred; AWS or GCP considered).
- Experience with Delta Lake / Parquet and medallion (Bronze–Silver–Gold) lakehouse design on platforms such as Microsoft Fabric or Databricks, including table optimization and maintenance.
- Experience with data orchestration tools (e.g., Apache Airflow, Azure Data Factory, or similar).
- Familiarity with vector databases, embedding pipelines, or Retrieval\-Augmented Generation (RAG) architectures.
- Experience with AI/ML frameworks and LLM tooling — e.g., scikit\-learn and XGBoost/LightGBM for predictive ML (PyTorch/TensorFlow a plus); MLflow for experiment tracking and model management; Azure OpenAI and Anthropic Claude APIs; and agent/orchestration frameworks such as Semantic Kernel or LangChain (or equivalent).
- Working knowledge of API integration and data ingestion from third\-party platforms.
- Familiarity with a BI and semantic\-modeling platform (e.g., Power BI, Tableau, or Looker); hands\-on knowledge of Power BI semantic modeling — DAX, TMDL, and XMLA endpoints — as an analytics serving layer is a strong plus.
- Proficiency with version control and collaborative development workflows (e.g., Git); familiarity with CI/CD practices a plus.
- Working knowledge of data governance, security, and PII/sensitive\-data handling in a production environment.
Analytical \& Functional Skills
- Ability to translate ambiguous business problems into structured, scalable data solutions.
- Strong debugging and troubleshooting skills across the full data stack.
- Comfort working with large, complex, and sometimes messy datasets in a production environment.
Communication \& Interpersonal Skills
- Ability to communicate technical concepts clearly to non\-technical stakeholders, including product managers and business leaders.
- Collaborative working style with a strong sense of ownership and follow\-through.
- Comfortable operating with autonomy in a lean, fast\-moving team environment.
Other Requirements
- Must be authorized to work in the United States
Preferred Qualifications
- Experience with real\-time or streaming data pipelines (e.g., Kafka, Spark Streaming, or similar).
- Exposure to AI\-assisted development tooling (e.g., Claude Code, GitHub Copilot) and comfort using these tools in daily workflows.
- Experience supporting multi\-unit restaurant, retail, or franchise operators with data and analytics.
- Relevant certifications in cloud platforms, data engineering, or AI/ML (e.g., Azure Data Engineer Associate, AWS Certified ML Specialty).
Competencies \& Success Indicators
Technical Depth with Practical Impact
- This role doesn’t just build technically sound solutions \- they build things that work reliably in production and deliver measurable value to the business and our clients.
- Pipelines are stable, well\-documented, and require minimal reactive firefighting after launch.
- AI/ML solutions deployed into production meet defined performance benchmarks and are monitored for drift.
- Technical decisions are explained in terms of business impact, not just engineering merit.
Ownership Mindset
- Takes full responsibility for the solutions they build, from design through delivery and ongoing operational health.
- Proactively identifies data quality issues and proposes solutions before they surface downstream.
- Sees work through to completion—not just to “ship,” but to adoption and operational stability.
- Surfaces architectural improvements or technical debt without waiting to be asked.
Collaborative Engineering
- Works effectively across technical and non\-technical teams to deliver shared outcomes.
- Translates data concepts into plain language for Product, CS, and leadership audiences.
- Actively participates in technical planning, retrospectives, and cross\-team discussions.
- Shares knowledge generously and contributes to a culture of continuous learning.
AI Curiosity \& Adaptability
- Stays current with the fast\-moving AI/ML landscape and applies emerging capabilities thoughtfully.
- Regularly evaluates new AI tools and frameworks for potential application at Rosnet.
- Comfortable experimenting, iterating, and failing fast in pursuit of better solutions.
- Applies AI\-assisted development tools to increase personal and team productivity.
Work Environment \& Physical Requirements
- This position is primarily performed in an office\-based environment. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions of the role in accordance with the Americans with Disabilities Act (ADA) and applicable state laws.
- Primarily sedentary role with extended periods of computer use.
- Hybrid work model; expected to work from the Kansas City office on a regular cadence as defined by department leadership.
- Ability to attend virtual and in\-person meetings as required, including occasional cross\-functional or company\-wide gatherings.
Compliance \& Equal Opportunity Statement
- Rosnet is an Equal Opportunity Employer. We are committed to creating a diverse and inclusive workplace. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, age, or any other characteristic protected by applicable federal, state, or local law.
- This job description is not intended to be a comprehensive list of all duties, responsibilities, or qualifications. Management reserves the right to modify, add, or remove duties as business needs change. This document does not constitute an employment contract.
- Rosnet participates in E\-Verify.
Role Details
About This Role
Data Engineers build the pipelines that feed AI models. They design ETL workflows, manage data lakes, and ensure training and inference data is clean, timely, and accessible. Without good data engineering, AI projects fail. It's that simple.
The AI era has expanded the data engineer's scope far beyond batch ETL jobs. You're building real-time embedding pipelines for RAG systems, managing vector databases, ensuring training data quality at scale, and building the infrastructure that lets ML teams iterate on data as fast as they iterate on models. Data quality is the biggest predictor of model quality, and you're the person responsible for it.
Across the 3,708 AI roles we're tracking, Data Engineer positions make up 1% of the market. At ROSnet, this role fits into their broader AI and engineering organization.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
What the Work Looks Like
A typical week includes: debugging a data pipeline that's producing stale embeddings for the RAG system, optimizing a Spark job that processes training data, building a data quality monitoring dashboard, meeting with the ML team to understand their next data requirements, and writing dbt models that transform raw event data into ML-ready features. The work is deeply technical and high-impact.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
Skills Required
SQL, Python, and distributed systems (Spark, Airflow, dbt) are core. Cloud data platforms (Snowflake, BigQuery, Redshift) are increasingly standard. Many AI-focused roles also want familiarity with vector databases and embedding pipelines. Understanding data modeling, pipeline orchestration, and data quality frameworks covers the essentials.
AI-specific data engineering skills include: building feature stores, managing training data versioning, implementing data lineage tracking, and building real-time embedding pipelines. Experience with streaming systems (Kafka, Flink) is valuable for real-time AI applications. Understanding ML data requirements (balanced datasets, data augmentation, evaluation set construction) makes you much more effective working with ML teams.
Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
Compensation Benchmarks
Data Engineer roles pay a median of $178,800 based on 40 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
ROSnet AI Hiring
ROSnet has 1 open AI role right now. They're hiring across Data Engineer. Based in Parkville, MO, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into Data Engineer roles include Backend Engineer, Database Administrator, Analytics Engineer.
From here, career progression typically leads toward Senior Data Engineer, ML Engineer, Data Platform Lead.
Master SQL and Python first. Then learn a distributed processing framework (Spark or its modern alternatives) and a pipeline orchestrator (Airflow, Dagster, Prefect). Build a portfolio project that demonstrates end-to-end pipeline construction: ingest, transform, validate, serve. If you want to specialize in AI data engineering, add vector databases and embedding pipelines to your skill set.
What to Expect in Interviews
Expect SQL deep-dives (query optimization, partitioning strategies, data modeling), Python coding focused on data pipeline patterns, and system design questions about building scalable ETL workflows. Companies with ML teams will ask about feature stores, embedding pipelines, and training data management. Be ready to discuss data quality monitoring, pipeline orchestration, and how you'd handle schema evolution in a production data lake.
When evaluating opportunities: Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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.
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