Job description

Quality Data Engineer Description - This role is responsible for leading the data engineering team supporting application projects and collaborating with cross-functional teams to ensure integration of data engineering deliverables with project outcomes. The role contributes to solution development for complex deals and oversees the development and maintenance of intricate databases. The role takes charge of resolving critical database incidents, produces data models, and leads model conversion efforts. The role also provides expert guidance, exercises independent judgment, and fosters productive relationships while mentoring lower-level employees. Responsibilities: Data Architecture Strategy • Design the enterprise-wide blueprint for how data is stored, integrated, accessed, and governed • Manage the technical platforms that enable downstream insights, solutions, etc • Design PS Quality data warehouses / data lakes • Determine architectural patterns (e.g., medallion architecture, data mesh, data fabric) • Establish data standards and automated interoperability rules Data Architecture & Platform Leadership • Designing data warehouses / data lakes that meets Quality Business Requirements • Define and implement enterprise-grade data architectures (batch, streaming, real-time) for large-scale structured and unstructured data. • Design scalable, secure, and high-performance data platforms supporting BI, advanced analytics, and AI/ML use cases. • Establish data modeling standards, and reusable frameworks across the organization.  Data Strategy & Transformation • Lead enterprise data strategy , aligning data initiatives with business, AI, and digital transformation goals. • Identify and prioritize high-value analytics and AI opportunities leveraging telemetry, operational, and product data. • Drive data monetization, standardization, and governance frameworks. • Define roadmap for modern data stack adoption (cloud-native, lakehouse, streaming, GenAI-ready architectures).  AI/ML Enablement & Industrialization • Partner closely with Data Scientists to productionize ML/AI models into scalable systems. • Build and optimize data pipelines, feature engineering frameworks, and MLOps workflows.  Engineering Execution & Innovation • Lead the design, development, and deployment of complex data pipelines and distributed systems. • Drive adoption of new technologies (GenAI, agentic systems, streaming architectures, data mesh) . • Ensure solutions meet performance, reliability, and cost optimization goals .  Governance, Security & Compliance • Ensure adherence to data governance, privacy, security, and compliance standards in alignment with HP Cybersecurity and privacy guidlines • Maintain master data management, access controls, audits, metadata, management, and data hierarchy • Establish data quality frameworks, lineage, observability, and monitoring mechanisms. • Implement best practices across data lifecycle management.  Cross-Functional Leadership & Influence • Influence executive leadership, architecture boards, and cross-functional stakeholders on data strategy decisions. • Act as a thought leader in data engineering and AI data ecosystems. • Represent the organization in industry forums, publications, and innovation initiatives.  Business Alignment • Translate business goals into platform capabilities • Faster automated analytics • Enhanced AI/ML readiness • Self-Service Tools • Operational Reporting • Enable data-driven decision making • Education & Experience Recommended: • Four-year or Graduate Degree in Computer Science, Information Systems, Engineering, Statistics/ Mathematics, Machine Learning, Data Analytics, and demonstrated competence. • 7-10 years of work experience, preferably in analytics, data science, reporting, or a related field. Technical Expertise • Strong experience in: • Cloud platforms: AWS, Azure (data services, analytics, storage) • Data platforms: Data Lakes, Lakehouse, Data Warehousing • ETL/ELT and pipeline orchestration • Programming: • Python, SQL (mandatory) • Scala/Java (good to have) • Experience with: • Streaming and real-time data systems • Data modeling and governance • MLOps / model deployment pipelines • Modern architecture (Data Mesh, Medallion, API-driven data services) Preferred Certifications • Data Analytics Certifications Knowledge & Skills • Agile Methodology • Amazon Web Services • Apache Spark • Automation • Big Data • Computer Science • Data Analysis • Data Architecture • Data Engineering • Data Modeling • Data Warehousing • Extract Transform Load (ETL) • Java (Programming Language) • Machine Learning • Microsoft Azure • NoSQL • Python (Programming Language) • Scalability • Software Engineering • SQL (Programming Language) Cross-Org Skills • Effective Communication • Results Orientation • Learning Agility • Digital Fluency • Customer Centricity Impact & Scope • Impacts function and leads and/or provides expertise to functional project teams and may participate in cross-functional initiatives. Complexity • Works on complex problems where analysis of situations or data requires an in-depth evaluation of multiple factors. Disclaimer • This job description describes the general nature and level of work performed in this role. It is not intended to be an exhaustive list of all duties, skills, responsibilities, knowledge, etc. These may be subject to change and additional functions may be assigned as needed by management. The pay range for this role is $105,050 to $161,800 USD annually with additional opportunities for pay in the form of bonus and/or equity (applies to United States of America candidates only). Pay varies by work location, job-related knowledge, skills, and experience. Benefits: HP offers a comprehensive benefits package for this position, including: • Health insurance • Dental insurance • Vision insurance • Long term/short term disability insurance • Employee assistance program • Flexible spending account • Life insurance • Generous time off policies, including; • 4-12 weeks fully paid parental leave based on tenure • 11 paid holidays • Additional flexible paid vacation and sick leave ( US benefits overview ) The compensation and benefits information is accurate as of the date of this posting. The Company reserves the right to modify this information at any time, with or without notice, subject to applicable law. Job - Data & Information Technology Schedule - Full time Shift - No shift premium (United States of America) Travel - 25% Relocation - Yes Equal Opportunity Employer (EEO) - HP, Inc. provides equal employment opportunity to all employees and prospective employees, without regard to race, color, religion, sex, national origin, ancestry, citizenship, sexual orientation, age, disability, or status as a protected veteran, marital status, familial status, physical or mental disability, medical condition, pregnancy, genetic predisposition or carrier status, uniformed service status, political affiliation or any other characteristic protected by applicable national, federal, state, and local law(s). Please be assured that you will not be subject to any adverse treatment if you choose to disclose the information requested. This information is provided voluntarily. The information obtained will be kept in strict confidence. For more information, review HP’s EEO Policy or read about your rights as an applicant under the law here: “ Know Your Rights: Workplace Discrimination is Illegal "

Requirements

Must-haves

Quality Data Engineer
Description -
Data Architecture Strategy
Design the enterprise-wide blueprint for how data is stored, integrated, accessed, and governed
Manage the technical platforms that enable downstream insights, solutions, etc
Design PS Quality data warehouses / data lakes
Determine architectural patterns (e.g., medallion architecture, data mesh, data fabric)
Establish data standards and automated interoperability rules
Data Architecture & Platform Leadership
Designing data warehouses / data lakes that meets Quality Business Requirements
Define and implement enterprise-grade data architectures (batch, streaming, real-time) for large-scale structured and unstructured data.
Design scalable, secure, and high-performance data platforms supporting BI, advanced analytics, and AI/ML use cases.
Establish data modeling standards, and reusable frameworks across the organization.
 Data Strategy & Transformation
Lead enterprise data strategy , aligning data initiatives with business, AI, and digital transformation goals.
Identify and prioritize high-value analytics and AI opportunities leveraging telemetry, operational, and product data.
Drive data monetization, standardization, and governance frameworks.
Define roadmap for modern data stack adoption (cloud-native, lakehouse, streaming, GenAI-ready architectures).
 AI/ML Enablement & Industrialization
Partner closely with Data Scientists to productionize ML/AI models into scalable systems.
Build and optimize data pipelines, feature engineering frameworks, and MLOps workflows.
 Engineering Execution & Innovation
Lead the design, development, and deployment of complex data pipelines and distributed systems.

Nice-to-haves

Preferred Certifications

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlmachine learningawsazurepythonsqljavaapiagile

Platform: eightfold
Effort: medium

Benefits

Benefits detected in the listing.

Health Insurance

Dental

Vision

Parental Leave