Quality Data Engineer

Shape enterprise data platforms and pipelines to power analytics and AI/ML outcomes while driving data quality, security, and governance.

Responsibilities

• Lead the data engineering team supporting application projects and coordinate with cross-functional partners to align data engineering deliverables with project outcomes
• Develop solutions for complex deals and contribute to solution design for enterprise requirements
• Oversee design, development, and maintenance of intricate databases
• Resolve critical database incidents, produce data models, and lead model conversion efforts
• Provide expert guidance, exercise independent judgment, and mentor lower-level employees to support team effectiveness
• Create an enterprise-wide blueprint for how data is stored, integrated, accessed, and governed
• Manage technical platforms that enable downstream insights and solutions
• Design PS quality data warehouses and data lakes
• Define architectural patterns such as medallion architecture, data mesh, and data fabric
• Establish data standards and automated interoperability rules
• Design data warehouses and data lakes that meet quality business requirements
• Define and implement enterprise-grade data architectures for batch, streaming, and real-time workloads for large-scale structured and unstructured data
• Build scalable, secure, high-performance data platforms for BI, advanced analytics, and AI/ML use cases
• Set data modeling standards and reusable frameworks across the organization
• Lead enterprise data strategy, aligning data initiatives with business goals, AI goals, and digital transformation priorities
• Identify and prioritize high-value analytics and AI opportunities using telemetry, operational, and product data
• Drive data monetization, standardization, and governance frameworks
• Define a roadmap 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
• Build and optimize data pipelines, feature engineering frameworks, and MLOps workflows
• Lead design, development, and deployment of complex data pipelines and distributed systems
• Drive adoption of new technologies including GenAI, agentic systems, streaming architectures, and data mesh
• Ensure solutions meet performance, reliability, and cost optimization goals
• Ensure compliance with 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
• Implement best practices across the data lifecycle
• Influence executive leadership, architecture boards, and cross-functional stakeholders on data strategy decisions
• Represent the organization in industry forums, publications, and innovation initiatives
• Translate business goals into platform capabilities
• Support outcomes including faster automated analytics, enhanced AI/ML readiness, self-service tools, and operational reporting
• Enable data-driven decision making

Requirements

• 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
• Strong experience with cloud platforms: AWS, Azure (data services, analytics, storage)
• Strong experience with data platforms: Data Lakes, Lakehouse, Data Warehousing
• Strong experience with ETL/ELT and pipeline orchestration
• Python and SQL (mandatory)
• Scala/Java (good to have)
• Experience with streaming and real-time data systems
• Experience with data modeling and governance
• Experience with MLOps and model deployment pipelines
• Experience with modern architecture: Data Mesh, Medallion, API-driven data services

Technologies

• AWS, Azure
• Data Lakes, Lakehouse, Data Warehousing
• ETL/ELT
• Python, SQL
• Scala, Java
• Streaming and real-time data systems
• Data Mesh, Medallion
• API-driven data services
• GenAI, agentic systems
• MLOps, MLOps workflows
• Apache Spark
• NoSQL
• Agile Methodology

Benefits

• Health insurance, dental insurance, vision insurance
• Long term and 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)

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 in-depth evaluation of multiple factors

Location: Spring, TX (onsite)

• Salary: USD 105,050 - 161,800 per year
• Experience: 7+ years
• Schedule: Full time
• Shift: No shift premium (United States of America)
• Travel: 25%
• Relocation: Yes
• Job category: Data & Information Technology