Leads the engineering implementations and architecture of cloud-based data engineering and advanced Artificial Intelligence (AI) & analytics platforms. Guides adoption of modern software engineering practices including Test-Driven Development (TDD), Continuous Integration/Continuous Delivery (CI/CD), and cloud-native architectures. Establishes and governs code quality metrics, deployment pipelines, and integration test coverage. Provides delivery status visibility using tools including Jira, Confluence, and Tableau dashboards. Applies Agile delivery models including Scrum, Kanban, and Scaled Agile Framework (SAFe) for enterprise-scale initiatives. Advances DevOps practices through automation of build, test, and deploy workflows. Ensures integration of observability, logging, and monitoring capabilities across analytics platforms. Manages implementation and operation of advanced analytics, data platforms, and Software as a Service (SaaS) solutions supporting workforce data use cases. Evaluates and approves analytics, Artificial Intelligence (AI), and data science platforms including vendor-delivered and managed services. Partners with cybersecurity, risk, compliance, data architecture, and Centers of Excellence (CoE) teams to ensure secure and compliant handling of associate and workforce data., * Drives cloud based AI and advanced analytics strategy and delivery, leading cross functional teams to build scalable solutions, standardize data and model practices, and align capabilities with business priorities to improve decision making and operational efficiency.

• Acts as a trusted partner and product leader, defining vision and roadmaps, enabling team empowerment and collaboration, and balancing business value, technology goals, and delivery constraints to ensure adoption, customer satisfaction, and sustained organizational growth.
• Defines and communicates product vision, mission, and multi-year roadmap aligned to business priorities and technology transformation goals.
• Serves as a trusted technology delivery partner translating business vision into actionable capabilities that drive value, adoption, and associate engagement.
• Manages complex, cross-functional, enterprise-scale initiatives across distributed systems and microservices architectures.
• Balances business value, technology goals, quality, delivery cost, Minimum Viable Product (MVP), and timelines to ensure effective delivery capability.
• Establishes delivery milestones, timelines, capacity planning, and dependency management for complex initiatives.
• Promotes enterprise-standard analytics consumption and delivery practices across business units.
• Manages vendor relationships including contract negotiation, Service Level Agreement (SLA) governance, escalation management, and third-party platform oversight.
• Ensures execution of third-party product upgrades, patching schedules, license renewals, and enterprise platform rollouts to maintain stability, security, and compliance.

Software Engineering, Kanban, Reporting Dashboards, Agile Programming Methodologies, Scrum Project Management and Software Development, Integration Testing, Continuous Deployment/Delivery, Continuous Integration, Software Engineering, Atlassian JIRA, Bachelor’s degree in Computer Science, Engineering, Information Technology, Information Systems, Management Information Systems, or a closely related field (or foreign education equivalent) and six (6) years of experience as a Director, Software Engineering (or closely related occupation) architecting, building, deploying, and monitoring real-time and batch pipelines for data engineering.

Or, alternatively, Master’s degree in Computer Science, Engineering, Information Technology, Information Systems, Management Information Systems, or a closely related field (or foreign education equivalent) and four (4) years of experience as a Director, Software Engineering (or closely related occupation) architecting, building, deploying, and monitoring real-time and batch pipelines for data engineering.

Skills and Knowledge:

Candidate must also possess:

· Demonstrated Expertise architecting, building, and deploying data pipelines on AWS cloud using AWS services (IAM, Glue, S3, Lambda, and EKS), Jenkins, Python, Airflow and Dynamo DB.

· Demonstrated Expertise implementing Cloud-based ML or Data Engineering pipelines using Amazon Web Services (AWS) and using EMR, RDS PostgreSQL, Redshift, Athena, and Step functions.

· Demonstrated Expertise developing real time streaming and batch data pipelines in support of data analytics, Machine Learning (ML), and Artificial Intelligence (AI) processing using Kafka, Apache Spark, Python, and AWS.

· Demonstrated Expertise building and maintaining data engineering pipelines or AI pipelines using Jenkins, Terraform, SQS, API Gateway, and Dynamo DB.

PE1M2

Please be advised that Fidelity’s business is governed by the provisions of the Securities Exchange Act of 1934, the Investment Advisers Act of 1940, the Investment Company Act of 1940, ERISA, numerous state laws governing securities, investment and retirement-related financial activities and the rules and regulations of numerous self-regulatory organizations, including FINRA, among others. Those laws and regulations may restrict Fidelity from hiring and/or associating with individuals with certain Criminal Histories.