Quality Data Engineer

HP•Spring, TX

1d•$73,800 - $113,650

About The Position

This role leads the data engineering team supporting application projects and collaborates with cross-functional teams to integrate data engineering deliverables with project outcomes. It contributes to solutions for complex deals and oversees the development and maintenance of intricate databases. The role resolves critical database incidents, produces data models, leads model conversion efforts, provides expert guidance, exercises independent judgment, builds productive relationships, and mentors lower-level employees. The position leads enterprise data architecture and strategy, including the design of data warehouses and lakes, scalable data platforms, data modeling standards, and reusable frameworks. It aligns data initiatives with business, AI, and digital-transformation goals; advances modern data-stack adoption; and enables BI, analytics, AI/ML, and self-service capabilities. It also leads development of complex data pipelines and distributed systems, supports productionization of AI/ML models, and drives performance, reliability, and cost optimization. The role establishes governance, security, privacy, compliance, data quality, lineage, observability, and lifecycle-management practices, and influences executive and cross-functional stakeholders on data strategy. The listed schedule is full time, with 25% travel and relocation offered. The stated annual pay range is $73,800 to $113,650 USD, with additional opportunities for bonus and/or equity for candidates in the United States.

Requirements

Four-year or graduate degree in Computer Science, Information Systems, Engineering, Statistics/Mathematics, Machine Learning, or Data Analytics, or demonstrated competence.
Four years of work experience, preferably in analytics, data science, reporting, or a related field.
Strong experience with cloud platforms, including AWS and Azure data services, analytics, and storage.
Experience with data lakes, lakehouses, and data warehousing.
Experience with ETL/ELT and pipeline orchestration.
Python and SQL experience; SQL is mandatory.
Experience with streaming and real-time data systems, data modeling and governance, MLOps/model deployment pipelines, and modern architectures such as Data Mesh, Medallion, and API-driven data services.
Knowledge of Agile methodology, Amazon Web Services, Apache Spark, automation, big data, computer science, data analysis, data architecture, data engineering, data modeling, data warehousing, ETL, machine learning, Microsoft Azure, NoSQL, scalability, software engineering, Python, and SQL.
Effective communication, results orientation, learning agility, digital fluency, and customer centricity.
Ability to work on complex problems requiring in-depth evaluation of multiple factors and to lead or provide expertise to functional project teams.

Nice To Haves

Scala or Java experience.
Data Analytics certifications.

Responsibilities

Lead the data engineering team supporting application projects and coordinate with cross-functional teams to integrate data engineering deliverables with project outcomes.
Contribute to solution development for complex deals and oversee development and maintenance of intricate databases.
Resolve critical database incidents, produce data models, and lead model conversion efforts.
Design enterprise-wide approaches for storing, integrating, accessing, and governing data, including Quality data warehouses and data lakes.
Manage technical platforms that enable downstream insights and solutions; select architectural patterns and establish data standards and automated interoperability rules.
Define and implement enterprise-grade batch, streaming, and real-time data architectures for large-scale structured and unstructured data.
Design scalable, secure, high-performance data platforms for BI, advanced analytics, and AI/ML use cases.
Establish data modeling standards and reusable frameworks across the organization.
Lead enterprise data strategy and align data initiatives with business, AI, and digital-transformation goals.
Identify and prioritize 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 machine-learning and AI models; build and optimize data pipelines, feature-engineering frameworks, and MLOps workflows.
Lead the design, development, and deployment of complex data pipelines and distributed systems; encourage technology adoption and ensure performance, reliability, and cost optimization.
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, lineage, observability, and monitoring practices.
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 supporting automated analytics, AI/ML readiness, self-service tools, operational reporting, and data-driven decisions.
Provide expert guidance, exercise independent judgment, foster productive relationships, and mentor lower-level employees.