Job Summary:
The Data Engineer III independently designs, develops, implements, and supports complex enterprise data pipelines, data models, and analytical solutions that deliver reliable, actionable information to the business. This role contributes to solution architecture, strengthens data quality and governance, and applies hands-on artificial intelligence (AI) experience to build AI-ready data assets and integrate AI capabilities into data engineering workflows. The Data Engineer III owns technical delivery within assigned initiatives, resolves non-routine problems, and collaborates with business and technology partners within established architecture, security, and governance standards.
Essential Functions:
- Independently design, develop, test, deploy, and support scalable ETL/ELT pipelines, data models, and analytical data assets; improve performance, reliability, and maintainability throughout the delivery lifecycle.
- Serve as a key contributor to data stewardship workgroups and cross-functional teams supporting data management and governance.
- Promote metadata management and data quality practices through knowledge-sharing sessions, technical guidance, and collaboration across the organization.
- Translate stakeholder priorities, business definitions, and data quality requirements into appropriate data models, technical specifications, and engineering solutions.
- Own the technical delivery of assigned data governance engineering initiatives, including metadata cataloging and data quality solutions, within established program standards.
- Analyze data and business processes to develop and maintain glossary definitions, policies, standards, guidelines, and workflows that support data management and governance programs.
- Lead the development and maintenance of metadata catalogs for assigned data domains, including technical metadata, business definitions, and data lineage.
- Identify and quantify data-related issues, assess business impact, and collaborate with stakeholders to develop and implement remediation plans.
- Partner with subject matter experts, end users, and data modelers to define business rules and implement automated controls that measure and improve data quality.
- Lead the investigation and resolution of reported data quality issues; develop and report metrics that demonstrate remediation progress, data quality trends, and adherence to established standards.
- Set objectives and manage technical delivery for assigned work, accounting for dependencies and impacts on related teams. Contribute to operational goals and work independently within established procedures, seeking direction for new domains or unique assignments.
- Resolve ambiguous, non-routine technical problems through analysis, data trends, and sound engineering judgment. Improve work methods based on experience, engage appropriate resources, and escalate risks or decisions outside established boundaries.
- Build and maintain governed, AI-ready datasets and pipelines for AI and machine learning use cases, including data preparation, validation, lineage, and access controls; collaborate with data science and application teams on integration requirements.
- Implement and support approved AI capabilities within data engineering solutions, such as AI-assisted data quality checks, metadata enrichment, or unstructured data processing. Validate outputs against defined acceptance criteria and monitor accuracy, reliability, latency, and cost.
- Use approved AI-assisted development tools to support coding, testing, troubleshooting, and documentation. Independently review and test generated outputs, protect sensitive data, and follow established security, privacy, and responsible AI requirements.
- Perform any other job duties as requested
Education and Experience:
- Bachelor’s degree in Computer Science or a related field, or equivalent years of relevant work experience, is required.
- A minimum of five (5) years of experience designing and developing ETL/ELT and/or business intelligence solutions is required.
- Demonstrated experience across requirements definition, design, implementation, testing, deployment, and production support of complex database, data integration, business intelligence, and analytical solutions is required.
- Demonstrated hands-on experience independently implementing and supporting AI-enabled data engineering solutions in a production environment is required. Experience must include preparing and governing data, integrating AI services or models into data workflows, validating outputs, and monitoring operational performance; familiarity with AI concepts or use of AI chat tools alone does not satisfy this requirement.
- Healthcare payer experience is preferred.
- Experience working within a formal data warehouse and data governance environment is preferred.
Competencies, Knowledge and Skills:
- Advanced SQL skills, including complex query development, troubleshooting, and performance optimization.
- Proficiency in Python or a comparable programming language for data transformation, automation, testing, and integration with AI services or model APIs.
- Demonstrated ability to develop moderately complex to complex data profiling, data quality, and metadata analysis processes using industry-standard tools.
- Ability to manage technical deliverables across multiple medium- to large-scale projects, prioritize competing demands, and communicate dependencies and delivery risks promptly.
- Strong understanding of data management and governance, including 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 defined quality measures; identify inaccurate or unsupported outputs, data leakage, and privacy risks, and implement appropriate validation and human review controls.
- Proficiency with version control, code review, automated testing, deployment practices, and operational monitoring for data pipelines and AI integrations.
- Ability to build effective working relationships with business and technology stakeholders at all organizational levels.
- Strong analytical and problem-solving skills, sound technical judgment, and attention to detail.
- Strong interpersonal, written, and verbal communication skills, including active listening, critical thinking, and the ability to explain technical concepts to nontechnical audiences.
- Ability to prioritize and execute work independently while contributing effectively in a collaborative, team-oriented environment.
- Customer-focused approach with a commitment to reliable delivery, responsive support, and business value.
Working Conditions:
- General office environment; may be required to sit or stand for extended periods of time
Compensation Range:
$94,100.00 - $164,800.00
CareSource takes into consideration a combination of a candidate’s education, training, and experience as well as the position’s scope and complexity, the discretion and latitude required for the role, and other external and internal data when establishing a salary level. In addition to base compensation, you may qualify for a bonus tied to company and individual performance. We are highly invested in every employee’s total well-being and offer a substantial and comprehensive total rewards package.
Compensation Type (hourly/salary):
Salary
Organization Level Competencies
This job description is not all inclusive. CareSource reserves the right to amend this job description at any time. CareSource is an Equal Opportunity Employer. We are dedicated to fostering an environment of belonging that welcomes and supports individuals of all backgrounds.
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