Senior Data Engineer- Fusion Experience

Oracle is seeking a Senior Data Engineer to design and build scalable data pipelines and data solutions with an emphasis on data governance, validation, and quality assurance. In this Cleveland, OH onsite role, you will work within an agile environment to develop, maintain, and troubleshoot data solutions focused on performance, security, and reliability.

What you’ll do

• Design and build data pipelines that enable optimal data processing from a variety of data sources.
• Collaborate with cross-functional teams to identify data requirements and align with project or initiative business objectives.
• Independently analyze, design, and troubleshoot data flows based on business needs.
• Review business requirements and translate them into technical specifications.
• Adjust data collection processes involving indexing and query optimization to improve performance.
• Build Extract, Transform, and Load (ETL) pipelines for efficient data collection and extraction.
• Analyze data sources through profiling to support successful pipeline builds.
• Define success and failure thresholds for data collection pipelines.
• Implement data governance policies and procedures for handling data throughout its lifecycle, including areas such as data retention, to maintain consistency, integrity, accuracy, and reliability.
• Redact Personally Identifiable Information (PII) and Protected Health Information (PHI) to meet data privacy and security standards.
• Apply data security measures to protect data from unauthorized access, use, disclosure, alteration, or destruction.
• Ensure data compliance with relevant laws, regulations, and industry standards.
• Implement rigorous data validation and integrity checks to identify and address data quality issues that could affect pipeline and model performance.
• Define data annotation and labeling processes independently to support data quality.
• Design and implement automation for data validation and governance.
• Independently design, develop, and optimize automated, scalable data pipeline architectures using ETL to produce reusable data products.
• Implement storage solutions to hold processed data for scalable, optimized access and analysis.
• Manage day-to-day data pipeline and storage operations.
• Write runnable code and conduct testing and debugging of data solutions.
• Independently manage work by monitoring timelines and deliverables to keep initiatives aligned with requirements.
• Prioritize work proactively and adapt to changes in resources or timelines, proposing adjustments to maintain efficiency.
• Work independently and collaboratively with other engineers in an agile setting to build scalable, efficient, cost-effective, and reliable data solutions.
• Build and maintain understanding of stakeholder and customer needs to support effective partnerships.
• Actively listen to diverse perspectives and ask questions to ensure clarity and shared understanding.
• Identify and address standard and non-standard issues using standard practices, escalating more complex issues as appropriate.
• Troubleshoot errors by analyzing data and information from multiple sources.
• Share knowledge and best practices, and pursue continuous learning to stay current with industry trends and tools.
• Seek and apply feedback and training to improve skills.
• Recommend updates to improve process efficiency and effectiveness, and request input on alternative approaches from team members.

Technologies

• Extract, Transform, and Load (ETL)
• Indexing
• Query optimizations