FREE ACCESS
5,000–10,000 jobs/day
See all jobs on Scoutfield
Search thousands of fresh jobs every day.
Discover
- Fresh listings
- Fast filters
- No subscription required
Create a free account and start exploring right away.
Data Engineer III
CareSource. Design, develop, test, deploy, and support scalable ETL/ELT pipelines, data models, and analytical data assets .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and developing ETL/ELT pipelines and data governance solutions, with a strong focus on data quality, metadata management, and AI integration. Proficient in advanced SQL and Python, capable of managing complex data projects and ensuring operational performance.
Highest-signal resume keywords
ETL/ELT Pipeline DevelopmentData Governance EngineeringAdvanced SQL SkillsAI-Enabled Data EngineeringData Quality Management
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
ETL DevelopmentData ModelingDatabase DesignData IntegrationData Quality AnalysisMetadata ManagementAI IntegrationPython ProgrammingSQL Query DevelopmentData Profiling
Soft Skills
Stakeholder CommunicationProblem SolvingTechnical GuidanceCollaborationProject Management
Tools & Technologies
Data Governance ToolsAI Development ToolsVersion Control SystemsAutomated Testing ToolsOperational Monitoring Tools
Industry Keywords
Healthcare Payer ExperienceData WarehouseData StewardshipData Quality StandardsMetadata Cataloging
Tech Stack
Tools & technologiesETLPythonSQL
About the role
Key responsibilities & impact- Design, develop, test, deploy, and support scalable ETL/ELT pipelines, data models, and analytical data assets
- Improve data solution performance, reliability, and maintainability throughout the delivery lifecycle
- Contribute to data stewardship workgroups and cross-functional data management and governance teams
- Promote metadata management and data quality practices through knowledge-sharing and technical guidance
- 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
- Develop and maintain glossary definitions, policies, standards, guidelines, and workflows for data management and governance programs
- Lead metadata catalogs for assigned data domains, including technical metadata, business definitions, and data lineage
- Identify and quantify data issues, assess business impact, and implement remediation plans with stakeholders
- Define business rules and implement automated controls to measure and improve data quality
- Investigate and resolve data quality issues; report remediation progress, quality trends, and standards adherence
- Build and maintain governed, AI-ready datasets and pipelines for AI and machine learning use cases
- Integrate and support approved AI capabilities in data engineering workflows, including AI-assisted quality checks, metadata enrichment, and unstructured data processing
- Validate AI outputs and monitor accuracy, reliability, latency, and cost
- Use approved AI-assisted development tools for coding, testing, troubleshooting, and documentation while protecting sensitive data and following security, privacy, and responsible AI requirements
- Manage technical delivery, dependencies, risks, and operational goals independently
- Resolve ambiguous, non-routine technical problems and escalate decisions outside established boundaries
- Perform other duties as requested
Requirements
What you’ll need- Bachelor’s degree in Computer Science or a related field, or equivalent years of relevant work experience, required
- Minimum of five (5) years of experience designing and developing ETL/ELT and/or business intelligence solutions
- 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
- Experience 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 comparable programming language
- Experience 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 defined quality measures
- Proficiency with version control, code review, automated testing, deployment practices, and operational monitoring
- Healthcare payer experience preferred
- Experience within a formal data warehouse and data governance environment preferred
Benefits
Comp & perks- Bonus tied to company and individual performance may be available
- Substantial and comprehensive total rewards package
- Total well-being support