Role Description IT Data Engineer III is a senior-level position on the Enterprise Data team, responsible for leading the design, development, and support of complex enterprise data solutions across modern cloud platforms. This role plays a key leadership role in optimizing CDCN's data systems and processes for performance, scalability, and reliability while advancing our technical capabilities with cloud-native technologies. The IT Data Engineer III serves as a technical expert and mentor, guides ad hoc teams, and leads efforts to improve data workflows, architecture, and integration across the organization. - Lead initiatives to design, develop, and maintain enterprise data warehouse systems and data integration pipelines used for analysis and reporting, leveraging Azure Fabric, Azure Synapse Analytics, and related Azure data platform technologies. - Design and implement scalable ETL/ELT (extract, transform, load) solutions across enterprise systems, leveraging advanced data engineering tools and technologies including Azure Data Factory, Databricks, and serverless data processing platforms. - Architect and optimize dimensional data models including fact tables, dimension tables, and slowly changing dimensions to support analytics and reporting requirements. - Lead response efforts to stakeholder data needs and system issues, serving as the primary technical contact for complex or escalated requests and ensuring alignment with business requirements and regulatory compliance. - Troubleshoot and resolve high-priority issues during regular business hours and as part of the after-hours on-call rotation, ensuring critical data systems remain operational and performing optimally. - Guide ad hoc teams in analyzing and improving data processes, system performance, and cost-efficiency through technical innovation, process redesign, and optimization of cloud infrastructure costs. - Provide technical mentorship and direction to peers and junior engineers across data projects and ongoing operations, fostering continuous learning and technical excellence. - Collaborate with engineers, analysts, and business partners to ensure that solutions align with strategic business goals, regulatory requirements, and industry best practices. - Implement and maintain data quality frameworks, validation rules, and monitoring mechanisms to ensure data accuracy, consistency, and reliability across enterprise systems. - Lead technical design reviews and code reviews, establishing and enforcing data engineering standards, best practices, and architectural principles. - Perform other duties as assigned, including leadership of special projects or enterprise-level initiatives. Qualifications - Bachelor's degree in Computer Science, Information Technology, Data Analytics, Data Science, Mathematics, or a related field required; equivalent professional experience will be considered. - 5–9 years of experience designing, developing, and supporting data platforms and enterprise data warehouse systems in production environments. - Proven ability to lead and serve as a technical expert on small to medium-sized projects and cross-functional initiatives. - Demonstrated experience mentoring junior engineers and contributing to team technical development. Requirements - Expert-level experience with Azure Fabric including lakehouse architecture, notebooks, Delta Lake, data pipelines, and data warehouse design. - Deep hands-on experience with Azure Synapse Analytics including dedicated SQL pools, serverless SQL pools, Synapse pipelines, and performance optimization. - Expert-level SQL scripting with advanced proficiency in writing complex queries, stored procedures, query optimization, and performance tuning. - Deep experience in ETL/ELT development and data pipeline architecture across enterprise systems. - Advanced dimensional data modeling expertise including star schema, snowflake schema, fact tables, dimension tables, and slowly changing dimensions. - Expert-level experience with cloud data platforms such as Azure (primary), AWS, or Oracle Cloud. - Extensive hands-on experience with tools and technologies including Azure Data Factory, Azure Fabric, Azure Synapse Analytics, Databricks, Azure Data Lake Storage Gen2, Azure DevOps, SSMS, and Power BI. - Expert-level understanding of data structures and data exchange formats including ANSI X12, JSON, XML, and industry-standard data formats. - Proficiency in Python or PySpark for data processing and transformation tasks. - Strong experience with version control systems (Git) and CI/CD practices for data engineering. - Familiarity with infrastructure as code (Terraform, ARM templates, or Bicep). - Experience implementing data quality frameworks and automated monitoring/alerting mechanisms. - Knowledge of data governance, security best practices, and regulatory compliance requirements. Benefits - A rewarding career helping others. - Fun and engaging work environment built on team unity. - Job satisfaction knowing you make a difference in the work you do and lives we serve. - Professional training to help advance your skills for career development. - Medical, Dental, and Vision Insurance. - Vacation accrued at 3.07 hours per pay period to use when accrued. - Two Paid Floating Holidays. - Nine Paid Federal Holidays. - Paid Safe Sick Time accrued at 1 hour per 30 worked to use when accrued. - Instant Earnings Option. - 401(k) Retirement plan & discretionary company match. - Company-Paid Life Insurance. - Supplemental Life, Accident, Critical Illness, and Hospital benefits. - Short and Long-Term Disability. - Paid Parental Leave. - Flexible Spending Account. - Employee Assistance Program. - Pet Insurance.

• design, build, and scale modern cloud-based data platforms • lead the architecture and operational management of the Snowflake data warehouse platform • develop robust ELT/ETL pipelines to deliver high-quality data • manage Snowflake access controls, roles, permissions, and secure data sharing practices

• Develop automated pipelines to ingest 100% of the data from the Risk tool into GCP. • Design and provide governed views in the Analytics layer (BigQuery), ensuring data integrity and quality for downstream consumption. • Implement metrics based on complex SQL logic and business rules. • Develop semantic indicators using LLMs via GCP (Vertex AI) for categorization and analysis of unstructured data. • Work directly with the software engineering team to ensure the solution aligns with the company's architecture and security standards.

• Contributing to a high-scale, complex product • Seeing the real-time impact of your work