Principal Data Engineer

Salary: $142,000 - 194,000 per year Requirements:

• We require 11 years of experience in software engineering, data engineering, data science, computer science, or information systems.
• Alternatively, we accept a bachelors degree in Software Engineering, Data Science, Computer Science, Information Systems, or a related field plus 7 years of relevant experience.
• Alternatively, we accept a masters degree in Software Engineering, Data Engineering, Data Science, Computer Science, Information Systems, or a related field plus 5 years of relevant experience.
• Alternatively, we accept a doctorate in Computer Science, Data Science, Software Engineering, Information Systems, or a related field plus 3 years of relevant experience.
• We look for strong data modeling skills and the ability to design effective data models for accurate, efficient data management.
• We value innovation and the ability to generate or support new ideas, technologies, or processes that drive organizational growth.
• We seek experience creating and refining prototypes for validation and stakeholder feedback.
• We require the ability to build and manage robust API integrations for seamless interoperability between systems.
• We expect knowledge of cloud computing, including deploying, managing, and securing cloud environments and applications.
• We prefer candidates with 12 years of experience in software engineering, data engineering, data science, computer science, or information systems.
• Alternatively, we prefer a bachelors degree in a related field plus 8 years of relevant experience.
• Alternatively, we prefer a masters degree in a related field plus 6 years of relevant experience.

• We mentor less experienced team members to clarify data requirements and business objectives.
• We design and help build data infrastructure that improves processing across diverse data sources.
• We analyze, design, and troubleshoot data flows based on business needs.
• We participate in architecture, performance, and security reviews of technical solutions.
• We tune data collection processes, including indexing and query optimization, to improve performance.
• We build ETL pipelines to enable efficient and scalable data collection and extraction.
• We work with upstream and downstream teams to uphold agreed service levels.
• We manage relationships with data providers.
• We design and implement data governance policies and procedures to support consistency, integrity, accuracy, and reliability across the data lifecycle.
• We lead redaction processes for PII and PHI data while supporting privacy and security compliance.
• We apply data minimization principles and safeguard data against unauthorized access, use, disclosure, alteration, or destruction.
• We ensure data complies with applicable laws, regulations, and industry standards.
• We design and implement data validation and integrity checks to reduce data quality issues that could affect pipelines and models.
• We help define data annotation and labeling processes to support data quality.
• We identify automation opportunities for validation and governance and implement them.
• We correct deviations and non-conformance when identified.
• We design, develop, and optimize automated, scalable, and efficient ETL-based data pipeline architectures.
• We implement advanced data storage solutions for scalable and efficient access and analysis.
• We manage day-to-day data pipeline and storage operations.
• We develop, maintain, and debug advanced data solutions in an agile environment.
• We review runnable code and support testing and debugging with less experienced team members.
• We evaluate new technologies to build more robust data solutions.
• We enforce and document code standards and team guidance.
• We document design decisions and obtain broader architecture feedback before implementation.
• We gather data and evidence to secure required approvals.
• We manage moderately complex tasks, monitor timelines and deliverables, and keep work aligned to requirements.
• We delegate, track, and prioritize work across multiple projects while providing technical oversight.
• We collaborate across the organization to align expectations and achieve shared goals.
• We use our understanding of business leaders, stakeholders, and customers to ensure solutions meet needs.
• We support inclusivity by seeking and valuing diverse perspectives.
• We solve moderately complex issues by analyzing a wide range of data and information.
• We escalate unresolved or critical issues with a thorough assessment and proposed solutions.
• We review, contribute to, and document problem-solving strategies.
• We pursue learning opportunities to expand our skills and stay current with industry trends and best practices.
• We seek feedback and training to continuously improve our capabilities.
• We coach and mentor junior team members to promote learning and knowledge sharing.
• We develop and recommend process improvements to increase efficiency and effectiveness across teams.
• We solicit feedback on alternative approaches and methods for ongoing improvement.
• We participate in candidate interviews, assess candidates, and provide hiring recommendations.

• API
• Cloud
• ETL
• Support
• LESS
• Oracle
• Security
• Flow

More:

hackajob is collaborating with Oracle to connect exceptional professionals with this opportunity. We work in an agile environment with other engineers to build scalable, efficient, cost-effective, and reliable data solutions. This role focuses on data infrastructure, ETL pipeline design, governance, validation, and solution engineering, with opportunities to contribute to architecture reviews, documentation, process improvement, mentoring, and talent development.

last updated 37 week of 2026