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About This Role
When you’re the best, we’re the best. We instill an environment where employees feel engaged, satisfied and able to contribute their unique skills and talents while living and working as their authentic selves. We provide extensive opportunities for personal and professional development, building both employee competence and organizational capability to fuel exceptional performance through an inclusive environment both now and in the future.
Summary
In this role, you will help build and enhance a modern, AI\-ready data enablement platform that supports cross\-domain analytics, governed data products, and reusable engineering patterns across the enterprise. You will enable data producers and analytics teams through guided pathways, reusable accelerators, metadata\-driven frameworks, CI/CD patterns, Databricks Asset Bundles, dbt transformation standards, Unity Catalog governance, and Starburst/Trino analytical access. You will focus on helping teams create trusted, governed, reusable, and AI\-ready derivative data products that support analytics, reporting, GenAI, semantic search, and emerging agentic platform use cases. As a hands\-on senior individual contributor, you will combine platform engineering, data transformation, governance, and enablement to drive scalable and repeatable data product delivery across the organization.
Responsibilities
- Build and support scalable data engineering solutions using Azure Databricks, PySpark, SQL, Delta Lake, Azure Data Factory, dbt, and Unity Catalog.
- Improve metadata\-driven Azure Data Factory and Databricks patterns for orchestration, configuration, monitoring, restartability, and operational support.
- Develop reusable accelerators including CI/CD templates, Databricks Asset Bundle patterns, deployment automation, environment configuration, and data product onboarding templates.
- Design, develop, and support dbt models, macros, tests, documentation, and transformation standards for governed analytical data products.
- Provide guidance on appropriate technology selection and implementation patterns across dbt, Databricks notebooks and workflows, Delta Live Tables, Spark, and Starburst/Trino.
- Support cross\-domain analytics initiatives by transforming source\-refined data into trusted, reusable, business\-aligned derivative data products.
- Leverage Unity Catalog to establish and support governed catalogs, schemas, tables, lineage, access controls, naming standards, and certification practices.
- Support Starburst/Trino as an analytical and federated query layer for governed enterprise data consumption.
- Apply Azure DevOps, Git, CI/CD, and Infrastructure as Code (IaC) practices to create repeatable, testable, and environment\-aware platform delivery processes.
- Troubleshoot and resolve production issues related to orchestration, transformations, data quality, access management, query performance, deployments, and operational workflows.
- Collaborate with data engineering, analytics, platform, governance, and business teams to establish reusable, scalable, and supportable data engineering patterns.
- Contribute to the evolution of enterprise data engineering standards, governance practices, observability capabilities, and AI\-ready data product frameworks.
Qualifications
- Relevant degree preferred.
- 5 or more years of hands\-on data engineering experience building production\-grade data platforms, pipelines, or analytical data products required.
- Strong experience with Azure Databricks, PySpark, Spark SQL, Delta Lake, Azure Data Factory, SQL, and dbt required.
- Experience with Azure DevOps, Git, pull request workflows, CI/CD pipelines, and release management practices required.
- Working knowledge of lakehouse architecture, metadata management, data governance, lineage, access control, and operational support required.
- Demonstrated ability to function as a senior individual contributor with strong ownership, technical judgment, and cross\-functional collaboration skills required.
- Experience supporting enterprise\-scale analytical platforms and governed data product delivery preferred.
- Experience with Unity Catalog, Starburst/Trino, Pulumi or other Infrastructure as Code tools, Databricks Asset Bundles, Apache Iceberg concepts, and AKS/Kubernetes\-based platform operations preferred.
- Experience building reusable frameworks, accelerators, templates, or platform capabilities for engineering teams preferred.
- Experience preparing governed structured data for AI/ML, GenAI, Retrieval\-Augmented Generation (RAG), semantic search, copilots, or agentic workflows preferred.
- Experience within healthcare, analytics, supply chain, finance, or other regulated enterprise environments preferred.
- Strong problem\-solving, communication, and collaboration skills with the ability to influence technical direction and establish best practices preferred.
- You must be authorized to work in the United States without sponsorship.
\#LI\-JB1
Estimated Hiring Range:
At Vizient, we consider skills, experience, and organizational needs in our compensation approach. Geographic factors may adjust the range estimate and hires typically fall below the top range. Compensation decisions are tailored to individual circumstances. The current salary range for this role is $102,400\.00 to $179,000\.00\.
This position is also incentive eligible.
Vizient has a comprehensive benefits plan! Please view our benefits here:
http://www.vizientinc.com/about\-us/careers
Equal Opportunity Employer: Females/Minorities/Veterans/Individuals with Disabilities
The Company is committed to equal employment opportunity to all employees and applicants without regard to race, religion, color, gender identity, ethnicity, age, national origin, sexual orientation, disability status, veteran status or any other category protected by applicable law.
Salary Context
This $102K-$179K range is below the median for Data Engineer roles in our dataset (median: $160K across 37 roles with salary data).
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 3,823 AI roles we're tracking, Data Engineer positions make up 1% of the market. At Vizient, Inc., 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 266 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($140K) sits 32% below the category median. Disclosed range: $102K to $179K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Vizient, Inc. AI Hiring
Vizient, Inc. has 2 open AI roles right now. They're hiring across Data Engineer, AI/ML Engineer. Positions span Irving, TX, US, Edina, MN, US. Compensation range: $179K - $290K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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