Data Engineer III

CareSource•Dayton, OH

1d•$94,100 - $164,800

About The Position

The Data Engineer III independently designs, develops, implements, and supports complex enterprise data pipelines, data models, and analytical solutions that provide reliable, actionable information to the business. The role contributes to solution architecture, strengthens data quality and governance, and applies hands-on AI experience to create AI-ready data assets and integrate AI capabilities into data engineering workflows. The engineer owns technical delivery for assigned initiatives, resolves non-routine problems, and collaborates with business and technology partners while working within established architecture, security, and governance standards. The position develops and maintains governed datasets, pipelines, metadata catalogs, data quality solutions, and analytical assets. It translates stakeholder priorities and business requirements into models, specifications, and engineering solutions; supports stewardship and governance workgroups; identifies and remediates data issues; and defines measures to track quality and remediation. It also builds and supports AI-enabled data engineering solutions, validates their outputs, monitors operational performance, and follows established security, privacy, and responsible AI requirements. The role works independently within established procedures, manages technical delivery and dependencies across assigned work, and seeks direction for new domains or unique assignments. The position is based in a general office environment and may require sitting or standing for extended periods. The stated compensation range is $94,100 to $164,800, with potential eligibility for a performance-based bonus and a comprehensive total rewards package.

Requirements

Bachelor’s degree in Computer Science or a related field, or equivalent years of relevant work experience.
At least five years of experience designing and developing ETL/ELT and/or business intelligence solutions.
Demonstrated experience across requirements definition, design, implementation, testing, deployment, and production support of complex database, data integration, business intelligence, and analytical solutions.
Hands-on experience independently implementing and supporting AI-enabled data engineering solutions in a production environment, including preparing and governing data, integrating AI services or models into data workflows, validating outputs, and monitoring operational performance. Familiarity with AI concepts or AI chat tools alone does not meet this requirement.
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.
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 suitable 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 explaining technical concepts to nontechnical audiences.
Ability to prioritize and execute work independently while contributing effectively in a collaborative, team-oriented environment.
Customer-focused approach and commitment to reliable delivery, responsive support, and business value.

Nice To Haves

Healthcare payer experience.
Experience working within a formal data warehouse and data governance environment.

Responsibilities

Independently design, develop, test, deploy, and support scalable ETL/ELT pipelines, data models, and analytical data assets, improving performance, reliability, and maintainability throughout delivery.
Contribute to data stewardship workgroups and cross-functional data management and governance teams.
Promote metadata management and data quality practices through knowledge-sharing sessions, technical guidance, and organizational collaboration.
Translate stakeholder priorities, business definitions, and data quality requirements into data models, technical specifications, and engineering solutions.
Own technical delivery of assigned data governance engineering initiatives, including metadata cataloging and data quality solutions, within program standards.
Analyze data and business processes to develop and maintain glossary definitions, policies, standards, guidelines, and workflows supporting data management and governance.
Lead 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 on 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 investigation and resolution of reported data quality issues and develop and report metrics on remediation progress, quality trends, and adherence to standards.
Set objectives and manage technical delivery for assigned work, accounting for dependencies and impacts on related teams; contribute to operational goals and escalate risks or decisions beyond established boundaries.
Resolve ambiguous, non-routine technical problems using analysis, data trends, and engineering judgment; improve work methods and engage appropriate resources.
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 AI integration requirements.
Implement and support approved AI capabilities in data engineering solutions, validate outputs against acceptance criteria, and monitor accuracy, reliability, latency, and cost.
Use approved AI-assisted development tools for coding, testing, troubleshooting, and documentation; independently review and test generated outputs and protect sensitive data.
Perform other job duties as requested.