Senior AI Data Platform Engineer

Vizient, Inc.

Irving (TX)

On-site

USD 102,400 - 179,000

Full time

14 days+

Application generator

Turn this role into an interview — a resume and cover letter built around what this employer wants.

Get past ATS filters

Job summary

Vizient, Inc. is seeking a hands-on senior data engineer to build and enhance a modern AI-ready data enablement platform.

You will work across cross-domain analytics, governed data products, and scalable patterns, enabling data producers and analytics teams with guided pathways and reusable accelerators. You will apply Databricks, Unity Catalog, Starburst/Trino, dbt, and IaC practices to deliver repeatable data product delivery, while partnering with governance and business teams to raise data

Qualifications

5+ years hands-on data engineering building production-grade data platforms, pipelines, or analytical data products.
Experience with Azure Databricks, PySpark, Spark SQL, Delta Lake, Azure Data Factory, SQL, and dbt.
Experience with Azure DevOps, Git, PR workflows, CI/CD pipelines, and release management.
Knowledge of lakehouse architecture, metadata management, governance, lineage, access control, and operational support.
Senior individual contributor with ownership, technical judgment, and cross-functional collaboration.
Experience with Unity Catalog, Starburst/Trino, IaC tools, Databricks Asset Bundles, Apache Iceberg concepts, AKS/Kubernetes-based ops.
Experience building reusable frameworks, accelerators, templates, or platform capabilities.
Experience preparing governed data for AI/ML, GenAI, RAG, semantic search, copilots, or agentic workflows.
Experience in healthcare, analytics, supply chain, finance, or other regulated enterprise environments.
Strong problem solving, communication, and collaboration; ability to influence technical direction and best practices.
You must be authorized to work in the United States without sponsorship.

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 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.