Data Engineer III

This is a fully remote position, open to applicants in United States.

📋 Description

• Design, develop, test, deploy, and support scalable ETL/ELT pipelines, data models, and analytical data assets.

• Enhance the performance, reliability, and maintainability of data solutions throughout their delivery lifecycle.

• Participate in data stewardship workgroups and collaborate with cross-functional teams on data management and governance.

• Advocate for metadata management and data quality practices through knowledge sharing and technical guidance.

• Convert stakeholder priorities, business definitions, and data quality requirements into data models, technical specifications, and engineering solutions.

• Take ownership of the technical delivery for assigned data governance engineering initiatives, including metadata cataloging and data quality solutions.

• Create and maintain glossary definitions, policies, standards, guidelines, and workflows for data management and governance programs.

• Lead metadata catalogs for assigned data domains, encompassing technical metadata, business definitions, and data lineage.

• Identify and assess data issues, evaluate business impact, and execute remediation plans in collaboration with stakeholders.

• Establish business rules and implement automated controls to evaluate and enhance data quality.

• Investigate and resolve data quality challenges; report on remediation progress, quality trends, and adherence to standards.

• Build and sustain governed, AI-ready datasets and pipelines for AI and machine learning applications.

• Integrate and support sanctioned AI capabilities within data engineering workflows, such as AI-assisted quality checks, metadata enrichment, and unstructured data processing.

• Validate AI outputs and monitor parameters like accuracy, reliability, latency, and cost.

• Utilize approved AI-assisted development tools for coding, testing, troubleshooting, and documentation while safeguarding sensitive data and adhering to security, privacy, and responsible AI standards.

• Independently manage technical delivery, dependencies, risks, and operational objectives.

• Tackle ambiguous, non-routine technical challenges and escalate decisions beyond established boundaries.

• Perform additional duties as required.

⛳️ Requirements

• A Bachelor’s degree in Computer Science or a related field, or equivalent years of relevant work experience, is required.

• At least five (5) years of experience in designing and developing ETL/ELT and/or business intelligence solutions.

• Experience in requirements definition, design, implementation, testing, deployment, and production support of complex database, data integration, business intelligence, and analytical solutions.

• Hands-on experience in independently implementing and supporting AI-enabled data engineering solutions in a production setting.

• Experience in preparing and governing data, integrating AI services or models into data workflows, validating outputs, and monitoring operational performance.

• Advanced SQL skills, including complex query development, troubleshooting, and performance optimization.

• Proficiency in Python or a similar programming language.

• Experience in developing data profiling, data quality, and metadata analysis processes using industry-standard tools.

• Ability to manage technical deliverables across multiple medium- to large-scale projects.

• Strong understanding of data management and governance, data quality, metadata management, lineage, stewardship, and access controls.

• Strong understanding of relational database theory, data modeling, and data warehouse design principles.

• Working knowledge of AI and machine learning data requirements and practical AI integration patterns, including model inference APIs and, where applicable, embeddings and retrieval-augmented generation (RAG).

• Ability to evaluate AI-enabled workflows using representative test data and established quality measures.

• Proficiency with version control, code review, automated testing, deployment practices, and operational monitoring.

• Healthcare payer experience is preferred.

• Experience within a formal data warehouse and data governance environment is preferred.

🏝️ Benefits

• Bonus tied to company and individual performance may be available.

• Substantial and comprehensive total rewards package.

• Total well-being support.