Quality Data Engineer

HP

Spring (TX)

On-site

USD 105,000 - 162,000

Full time

4 days ago

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Benefits offered by this job

Health insurance

Dental insurance

Vision insurance

Disability insurance

Employee assistance program (EAP)

Flexible spending account (FSA)

Life insurance

Parental leave (paid)

Holidays (11)

Vacation & sick leave

Job summary

HP is building and scaling data platforms in Spring, TX to enable insights, analytics, and AI- enabled capabilities. You will lead enterprise data engineering efforts across batch, streaming, and real-time use cases onsite in Spring, TX.

You will collaborate with cross-functional partners to modernize the data stack, establish standards, and enable scalable pipelines that support BI, analytics, and AI/ML systems.

Qualifications

Bachelor’s or higher in Computer Science, Information Systems, Engineering, Statistics/Mathematics, or related field.
7–10 years of experience in analytics, data science, reporting, or related fields.
Strong experience with Cloud platforms: AWS and Azure.
Strong experience with data platforms: Data Lakes, Lakehouse, Data Warehousing.
Proficient in ETL/ELT and pipeline orchestration.
Programming in Python and SQL is mandatory.
Experience with streaming/real-time data systems; knowledge of MLOps.
Familiarity with modern architecture patterns: Data Mesh, Medallion, API-driven data services.

Responsibilities

Lead enterprise data architecture and governance initiatives for HP’s data platforms.
Design scalable data warehouses and data lakes with modeling standards.
Define patterns like medallion architecture, data mesh, and data fabric.
Establish data standards and automated interoperability rules.
Build enterprise-grade data architectures for large-scale structured and unstructured data using batch, streaming, and real-time approaches.
Develop secure, high-performance data platforms for BI, analytics, and AI/ML use cases.
Lead data strategy aligned with business, AI, and digital transformation goals.
Partner with Data Scientists to productionize ML/AI models and enable MLOps pipelines.
Guide design and deployment of complex data pipelines and distributed systems.
Influence stakeholders on data strategy and governance within HP.

Skills

Python

SQL

ETL/ELT

Data architecture

Data governance

MLOps

Spark

Education

Four-year or Graduate Degree

Tools

AWS

Azure

Apache Spark

Data Lakes

Lakehouse

Data Warehousing

Job description

HP is building and scaling data platforms that help application teams deliver insights, analytics, and AI-enabled capabilities. In this onsite role in Spring, TX, you will lead enterprise data engineering efforts, shaping how data is architected, governed, and productionized across batch, streaming, and real-time use cases.

You will work with cross-functional partners to modernize the data stack, establish standards for interoperability and modeling, and enable scalable pipelines that support BI, advanced analytics, and AI/ML systems.

Key Responsibilities

Design an enterprise-wide blueprint for how data is stored, integrated, accessed, and governed.
Manage the technical platforms that enable downstream insights and solutions.
Design PS Quality data warehouses and data lakes, including data modeling standards and reusable frameworks.
Define architectural patterns such as medallion architecture, data mesh, and data fabric.
Establish data standards and automated interoperability rules.
Build enterprise-grade data architectures for large-scale structured and unstructured data using batch, streaming, and real-time approaches.
Develop scalable, secure, high-performance data platforms for BI, advanced analytics, and AI/ML use cases.
Lead enterprise data strategy aligned with business, AI, and digital transformation goals.
Identify and prioritize high-value analytics and AI opportunities using telemetry, operational, and product data.
Drive data monetization, standardization, and governance frameworks.
Define roadmaps for modern data stack adoption, including cloud-native, lakehouse, streaming, and GenAI-ready architectures.
Partner with Data Scientists to productionize ML/AI models into scalable systems and workflows (including feature engineering and MLOps).
Lead the design, development, and deployment of complex data pipelines and distributed systems.
Influence executive leadership, architecture boards, and cross-functional stakeholders on data strategy decisions.
Ensure adherence to data governance, privacy, security, and compliance standards in alignment with HP Cybersecurity and privacy guidelines.
Maintain master data management, access controls, audits, metadata management, and data hierarchy.
Establish data quality frameworks, lineage, observability, and monitoring mechanisms, and apply best practices across the data lifecycle.
Represent HP in industry forums, publications, and innovation initiatives, and translate business goals into platform capabilities.
Support complex problem solving that requires in-depth analysis of multiple factors; provide expertise to functional project teams and contribute to cross-functional initiatives.

Required Qualifications

Education: Four-year or Graduate Degree.
Education focus: Computer Science, Information Systems, Engineering, Statistics/Mathematics, Machine Learning, Data Analytics (demonstrated competence).
Experience: 7-10 years of work experience, preferably in analytics, data science, reporting, or a related field.
Strong experience with Cloud platforms: AWS and Azure (data services, analytics, storage).
Strong experience with data platforms: Data Lakes, Lakehouse, Data Warehousing.
Strong experience in ETL/ELT and pipeline orchestration.
Programming: Python and SQL (mandatory).
Additional: Scala/Java (good to have), Streaming and real-time data systems, data modeling and governance, and MLOps/model deployment pipelines.
Experience with modern architecture patterns: Data Mesh, Medallion, and API-driven data services.