Manager, Data Engineering

Capital Blue Cross; United States; Presencial; USD 140.000 - 190.000; Jornada completa

Descripción de la vacante

Capital Blue Cross is looking for a Manager, Data Engineering to lead the data engineering team and set the enterprise data platform strategy. You will guide the design, development, and operation of scalable, secure data solutions that support analytics, reporting, regulatory requirements, and AI initiatives.

You will establish engineering standards, drive modernization, mentor engineers, and partner with technology and business leaders to deliver trusted, governed data assets across the

Formación

• Extensive experience leading data engineering teams.
• Proven track record delivering enterprise data platforms and modern data architectures.
• Strong knowledge of data governance, security, and quality practices.

Responsabilidades

• Lead the design, development, and operation of enterprise data platforms.
• Drive modernization and data integration efforts with Snowflake and other platforms.
• Coach and manage data engineers; set standards and ensure reliability.
• Collaborate with governance, security, and stakeholders to ensure data quality and compliance.
• Develop and execute data engineering roadmaps aligned with business objectives.

Conocimientos

Snowflake

Data architecture

Cloud data engineering

Leadership

Data governance

Stakeholder management

ETL/ELT

Herramientas

Snowflake

Airflow

ETL pipelines

Git

CI/CD

Descripción del empleo

Base pay is influenced by several factors including a candidate's qualifications, relevant experience, and anticipated contributions to meet the needs of the business, along with internal pay equity and external market driven rates. The salary range displayed has not been adjusted for geographical location. This range has been created in good faith based on information known to Capital Blue Cross at the time of posting and may be modified in the future. Capital Blue Cross offers a comprehensive benefits packaging including Medical, Dental & Vision coverage, a Retirement Plan, generous time off including Paid Time Off, Holidays, and Volunteer time off, an Incentive Plan, Tuition Reimbursement, and more.

• Medical, Dental & Vision coverage
• a Retirement Plan
• generous time off including Paid Time Off, Holidays, and Volunteer time off
• an Incentive Plan
• Tuition Reimbursement
• and more

At Capital Blue Cross, we promise to go the extra mile for our team and our community. This promise is at the heart of our culture, and it's why our employees consistently vote us one of the "Best Places to Work in PA."

The Manager, Data Engineering, is responsible for leading Capital's Data Engineering team and enterprise data platform strategy. This role oversees the design, development, integration, modernization, and operation of scalable, secure, and reliable data solutions that support reporting, analytics, regulatory requirements, artificial intelligence initiatives, and business operations. The Manager establishes engineering standards, drives data modernization efforts, develops team capabilities, and partners with technology and business leaders to deliver trusted, governed, and accessible enterprise data assets.

Responsibilities and Qualifications

Data Engineering Strategy and Architecture

• Provide leadership for the design, implementation, operation, and continuous improvement of Capital's enterprise data engineering capabilities.
• Establish current and forward-looking technical direction, architecture standards, integration efficiencies and engineering practices supporting Snowflake and other enterprise data platforms to drive enhanced platform capabilities in partnership with stakeholders and business areas for meeting strategic objectives.

Data Engineering Delivery and Team Leadership

• Lead, coach, and develop a team of Data Engineers responsible for data platform delivery, operational support, modernization initiatives, and production reliability in cloud data engineering best practices.
• Establish performance expectations, technical standards, and development plans that promote continuous learning and engineering excellence that meets or exceeds corporate defined objectives.
• Manage team workload, resource allocation, priorities, and delivery commitments to ensure timely and high-quality outcomes.
• Provide technical leadership through solution reviews, design guidance, code reviews, and engineering governance processes.

Data Integration, Ingestion, and Modernization

• Lead the design, implementation, and evolution of enterprise data ingestion, integration, transformation, and data delivery frameworks.
• Establish reusable integration patterns supporting batch, API-based, file-based, streaming, event-driven, and other modern data exchange methods.
• Guide modernization efforts to simplify, automate, migrate or consolidate legacy data warehouse, integration, and reporting technologies.
• Develop and execute roadmaps in collaboration with stakeholders that support enterprise data platform adoption, onboard new data domains and build scalable capabilities with reduced technical debt.

Operational Excellence and Engineering Standards

• Establish and maintain engineering standards covering source control, testing, deployment, monitoring, documentation, release management, and production support.
• Establish and monitor operational metrics to ensure reliable enterprise data platforms availability and continuous improvement opportunities identified, prioritized and implemented. Execute on identified corrective actions.
• Promote the effective use of automation and approved AI-assisted development capabilities to improve engineering productivity and quality.

Data Governance, Security, and Quality

• Partner with Data Governance, Security, and business stakeholders to incorporate data quality, metadata, lineage, stewardship, privacy, and access controls into engineering solutions.
• Ensure compliance with enterprise requirements related to data protection, regulatory obligations, auditability, retention, and appropriate use of sensitive information.
• Support the delivery of governed, trusted, and documented data assets for enterprise reporting, analytics, applications, and artificial intelligence initiatives.

Stakeholder Partnership and Planning

• Develop and maintain the Data Engineering roadmap, aligning technology priorities with business, regulatory, operational, and strategic objectives.
• Partner with business leaders, technology teams, vendors, and data stakeholders to understand requirements, establish priorities, evaluate tool selections and deliver sustainable data solutions