Lead Data Engineer & AI Platform Architect

HP Inc.; Spring (TX); On-site; USD 105,000 - 162,000; Full time

Benefits offered by this job

Health insurance

Dental insurance

Vision insurance

Disability insurance

Employee assistance program

Flexible spending account

Life insurance

Parental leave

Paid holidays

Paid vacation and sick leave

Job summary

HP Inc. seeks a Quality Data Engineer to lead its data engineering group and drive enterprise data platforms. You will design data warehouses and lakes, orchestrate pipelines, and align data strategy with AI/ML initiatives, while mentoring staff and ensuring governance compliance.

You will collaborate with cross-functional teams, manage complex data projects, and help enable self-service analytics and real-time insights across the organization.

Qualifications

• Four-year or graduate degree in computer science, information systems, engineering, statistics/math, ML, or data analytics.
• 7–10 years of experience in analytics, data science, reporting, or related field.

Responsibilities

• Architect enterprise data solutions with batch, streaming, and real-time data.
• Lead data pipelines, scalable systems, and governance across data platforms.
• Partner with data scientists to productionize models and enable AI/ML use cases.
• Drive data strategy, monetization, and governance standards.

Skills

AWS

Azure

Python

SQL

Data Modeling

Data Warehousing

Apache Spark

Data Lakes

Lakehouse

Data Mesh

Medallion Architecture

MLOps

Streaming/Real-time

NoSQL

Java

Education

Four-year or Graduate Degree in CS/IS/Engineering/Statistics/Math/Data Analytics/ML

Tools

Data Lakes

Lakehouse

Data Warehousing

Apache Spark

Python

SQL

Job description

HP Inc. seeks a Quality Data Engineer to lead its data engineering group and drive enterprise data platforms. You will design data warehouses and lakes, orchestrate pipelines, and align data strategy with AI/ML initiatives, while mentoring staff and ensuring governance compliance.

You will collaborate with cross-functional teams, manage complex data projects, and help enable self-service analytics and real-time insights across the organization.