Manager, Data Engineering

Capital Blue Cross•Harrisburg, PA

2d•$102,310 - $190,015

About The Position

The Manager, Data Engineering leads Capital Blue Cross’s Data Engineering team and enterprise data platform strategy. The role oversees the design, development, integration, modernization, and operation of scalable, secure, and reliable data solutions supporting reporting, analytics, regulatory requirements, artificial intelligence initiatives, and business operations. The manager establishes engineering standards, drives modernization, develops team capabilities, and partners with technology and business leaders to deliver trusted, governed, and accessible enterprise data assets. The position provides technical direction for enterprise data engineering capabilities, including Snowflake and other data platforms, and leads the team’s delivery, operational support, modernization initiatives, and production reliability. It guides data ingestion, integration, transformation, and delivery frameworks; develops roadmaps for platform adoption and new data domains; and promotes engineering standards, automation, governance, security, data quality, and operational excellence. The manager partners with business leaders, technology teams, vendors, and data stakeholders to align technology priorities with business, regulatory, operational, and strategic objectives. The role communicates project status, technical risks, tradeoffs, and recommendations to technical and non-technical audiences. It operates in a professional office and remote-work environment, with routine in-person and virtual collaboration. Leadership or escalation support outside standard business hours may be required for significant production incidents, releases, or business-critical data delivery issues.

Requirements

Seven or more years of progressive experience in data engineering, data integration, data warehousing, or enterprise data platform delivery.
Three or more years of experience leading technical teams, including coaching, performance management, and delivery accountability.
Demonstrated experience designing, implementing, and operating production data solutions on Snowflake, applying its capabilities beyond basic data storage, SQL development, and traditional warehouse workloads.
Demonstrated ability to lead and develop a technical Data Engineering team, including coaching, work assignment, quality review, delivery accountability, and skills development.
Strong ability to establish and operationalize engineering standards, reusable patterns, delivery controls, and production-support practices.
Strong technical and architectural judgment concerning Snowflake within a broader enterprise data environment.
Strong communication skills, including explaining technical issues, architectural decisions, tradeoffs, risks, costs, and investment needs in clear business terms.
Ability to manage multiple complex initiatives and shifting priorities across modernization, operations, technical debt, regulatory delivery, and business demand.
Ability to balance strategic initiatives with operational responsibilities and deliver results in a complex environment.
Strong collaboration and relationship-building skills across business and technology teams.
Advanced knowledge of Snowflake architecture and capabilities, including ingestion, transformation, modeling, governed data sharing, security, workload management, performance optimization, monitoring, automation, interoperability, and cost-aware design.
Strong understanding of modern cloud and hybrid data architectures, including centralized, distributed, virtual, federated, shared, and API-enabled approaches.
Knowledge of modern data integration approaches, including batch, incremental, change data capture, API-based, file-based, streaming, event-driven, virtual, federated, and governed data-sharing patterns.
Strong understanding of automated testing, source control, CI/CD processes, and environment and release management.
Bachelor’s degree in Computer Science, Software Engineering, Information Systems, Data Engineering, or a related field, or equivalent relevant professional experience.

Nice To Haves

Knowledge of healthcare payer data, healthcare data standards, and enterprise SaaS integrations.
Knowledge of WhereScape or comparable data automation and data engineering frameworks.
Experience in a healthcare payer, insurance, or other regulated data environment.
Relevant Snowflake technical certification is strongly preferred.
Relevant cloud, data engineering, architecture, security, or DevOps certification is preferred.

Responsibilities

Lead the design, implementation, operation, and continuous improvement of enterprise data engineering capabilities.
Establish technical direction, architecture standards, integration efficiencies, and engineering practices for Snowflake and other enterprise data platforms.
Lead, coach, and develop Data Engineers responsible for platform delivery, operational support, modernization, and production reliability.
Set performance expectations, technical standards, and development plans; manage workload, resource allocation, priorities, and delivery commitments.
Provide technical leadership through solution reviews, design guidance, code reviews, and engineering governance.
Lead the design, implementation, and evolution of enterprise data ingestion, integration, transformation, and delivery frameworks.
Establish reusable integration patterns for batch, API-based, file-based, streaming, event-driven, and other data exchange methods.
Guide modernization to simplify, automate, migrate, or consolidate legacy data warehouse, integration, and reporting technologies.
Develop roadmaps with stakeholders for enterprise platform adoption, onboarding data domains, scalable capabilities, and reduced technical debt.
Establish and maintain standards for source control, testing, deployment, monitoring, documentation, release management, and production support.
Establish and monitor operational metrics, identify and prioritize improvement opportunities, and execute corrective actions.
Promote effective use of automation and approved AI-assisted development capabilities to improve engineering productivity and quality.
Partner with Data Governance, Security, and business stakeholders to incorporate data quality, metadata, lineage, stewardship, privacy, and access controls into solutions.
Ensure compliance with data protection, regulatory, auditability, retention, and sensitive-information requirements.
Support delivery of governed, trusted, and documented data assets for reporting, analytics, applications, and AI initiatives.
Maintain the Data Engineering roadmap and align technology priorities with business, regulatory, operational, and strategic objectives.
Partner with business leaders, technology teams, vendors, and data stakeholders to understand requirements, set priorities, evaluate tool selections, and deliver sustainable solutions.
Provide project SDLC gate visibility and communicate technical concepts, risks, tradeoffs, and recommendations to technical and non-technical audiences.
Complete other responsibilities and assignments as directed.