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About This Role
At NiCE, we don't limit our challenges. We challenge our limits. Always. We're ambitious. We're game changers. And we play to win. We set the highest standards and execute beyond them. And if you're like us, we can offer you the ultimate career opportunity that will light a fire within you.
So, what's the role all about?
As a NiCE AI Data Engineer you are responsible for supporting the use of the NiCE Interaction Analytics (NIA) product suite to prove out business use cases as part of Proofs of Concepts (POCs). The POC is used to support the NiCE sales efforts to demonstrate the value of the NIA product suite. In this capacity, you will work with the NiCE sales team to obtain unstructured Customer Experience (CX) interactions (audio, chats, emails, social media, etc.) from customers and transform the interactions into a format that can be processed by the NIA product suite. You will act as an Interaction Analytics data expert who understands key data development and the impact of downstream cases and value to be derived from having data generated from unstructured interactions. You will partner closely with the Customer Engagement Analytics (CEA) Research, Product and Services teams blending your data expertise with best\-in\-class technology advancements and client insights to accomplish the team's objectives.
AI Data Engineers are experts in the value derived from unstructured CX interactions. It is that expertise, along with technical fluency, that allow the Business Consulting team to build scalable, out of the box data structures that reveal operational and measurable outcomes. The responsibilities for this role includes the development of the underlying data structures, along with the ability to understand new and evolving client requests for improvement and the ability to incorporate those needs into a successful build and execution plan for each POC.
The ideal candidate is intellectually inquisitive, with strong communication, organization, analytical and technical capabilities. As a self\-starter, they are not afraid to roll up their sleeves to learn and validate evolving toolsets while maintaining focus on what success looks like with our ultimate goals for data solutions. As they gain and maintain system and data expertise, the roll will continue to open up for direct client interactions, feedback capture and build plans.
How will you make an impact?
- Serve as the focal point for managing the delivery of CX interactions and corresponding metadata utilizing various data delivery methods.
- Serve as the focal point for the development of pre\-defined data structures for automated analysis of large sets of CX interactions across all channels (audio, chat, email, social, etc.). This includes:
+ Manipulation of CX interaction metadate into approved formats
+ Manipulation of CX interactions into the NiCE Interaction Analytics platform
+ Manipulation of CX transcripts
- Utilize evolving tools from Research that support the development of the data structures.
- Provide feedback to the Research and Product teams on how we continue to evolve tooling and data integration with CEA products.
- Work with client requirements to understand new requests and how we can or cannot meet them.
- Some team members may work directly with clients as a Subject Matter Expert to discuss data solutions, expectations and best practices.
- Educate field teams (Consulting and Customer Success) on data solutions, definitions, best practices, etc.
- Provide input on the use of developed data models for both Product and Field teams to optimize the use of developed data assets.
- As the Enlighten AI team is focused on Data Innovation, team members may be asked to support other projects, solutions and/or deployments using other Enlighten AI solutions.
- Manage the utilization of the CX Proof of Concept (POC) VM servers.
Have you got what it takes?
- 3\+ years of software engineering experience *and/or* significant experience with business intelligence, analytics, and data visualization as a services provider
- Experience using Windows Server
- Experience with basic scripting in PowerShell, Command Line, or Python
- Knowledge of SQL preferred, such as using Data Manipulation Language and Data Definition Language
- Familiarity with Linux
- Familiarity with ssh and basic bash skills
- Experience with Docker, accessing AWS S3 buckets, and SFTP
- Experience with use of technology to build and analyze datasets for operational outcomes
- Excellent oral and written communication skills in English
- Ability to multi\-task and meet deadlines when supporting requests that create competing priorities
- Ability to develop and maintain good working relationships with cross\-functional teams
- Ability to communicate comfortably across all levels of corporate structure
- Demonstrated ability to take the initiative and work in a self\-directed manner
- Ability to thrive in a changing environment
What's in it for you?
Join an ever\-growing, market disrupting, global company where the teams – comprised of the best of the best – work in a fast\-paced, collaborative, and creative environment! As the market leader, every day at NiCE is a chance to learn and grow, and there are endless internal career opportunities across multiple roles, disciplines, domains, and locations. If you are passionate, innovative, and excited to constantly raise the bar, you may just be our next NiCEr!
Requisition ID: 10299
Reporting into: Vice President of Solution Sales
Role Type: Individual Contributor
*About NiCE*
*NICE Ltd. (NASDAQ: NICE) software products are used by 25,000\+ global businesses, including 85 of the Fortune 100 corporations, to deliver extraordinary customer experiences, fight financial crime and ensure public safety. Every day, NiCE software manages more than 120 million customer interactions and monitors 3\+ billion financial transactions.*
*Known as an innovation powerhouse that excels in AI, cloud and digital, NiCE is consistently recognized as the market leader in its domains, with over 8,500 employees across 30\+ countries.*
*NiCE is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, age, sex, marital status, ancestry, neurotype, physical or mental disability, veteran status, gender identity, sexual orientation or any other category protected by law.*
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 26,159 AI roles we're tracking, Data Engineer positions make up 1% of the market. At NiCE, 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 $208,300 based on 199 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
NiCE AI Hiring
NiCE has 19 open AI roles right now. They're hiring across AI/ML Engineer, AI Consultant, AI Software Engineer, Data Engineer. Positions span Remote, US, Hoboken, NJ, US, Atlanta, GA, US.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% of all AI roles offer remote work.
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 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 $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 $122,200. 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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