Director, Software Engineering

Fidelity Investments•Westlake, TX

About The Position

Leads engineering implementation and architecture for cloud-based data engineering and advanced AI and 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 through Jira, Confluence, and Tableau dashboards; and applies Agile delivery models such as Scrum, Kanban, and SAFe. Advances DevOps automation and ensures analytics platforms include observability, logging, and monitoring capabilities. Manages implementation and operation of advanced analytics, data platforms, and SaaS solutions supporting workforce data use cases. Evaluates and approves analytics, AI, and data science platforms, including vendor-delivered and managed services. Partners with cybersecurity, risk, compliance, data architecture, and Centers of Excellence 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. Acts as a trusted partner and product leader, defining product vision and roadmaps and balancing business value, technology goals, quality, delivery cost, MVP scope, and timelines. Manages complex enterprise initiatives across distributed systems and microservices, and oversees vendor relationships, service-level agreements, upgrades, patching, license renewals, and platform rollouts.

Requirements

Bachelor’s degree in Computer Science, Engineering, Information Technology, Information Systems, Management Information Systems, or a closely related field (or foreign equivalent), plus six years of experience as a Director, Software Engineering or in a closely related occupation architecting, building, deploying, and monitoring real-time and batch data engineering pipelines; alternatively, a relevant master’s degree (or foreign equivalent) plus four years of that experience.
Demonstrated expertise architecting, building, and deploying AWS cloud data pipelines using IAM, Glue, S3, Lambda, EKS, Jenkins, Python, Airflow, and DynamoDB.
Demonstrated expertise implementing cloud-based ML or data engineering pipelines using AWS, including EMR, RDS PostgreSQL, Redshift, Athena, and Step Functions.
Demonstrated expertise developing real-time streaming and batch data pipelines for data analytics, ML, and AI processing using Kafka, Apache Spark, Python, and AWS.
Demonstrated expertise building and maintaining data engineering or AI pipelines using Jenkins, Terraform, SQS, API Gateway, and DynamoDB.

Responsibilities

Lead cloud-based AI and advanced analytics strategy and delivery, including cross-functional teams building scalable solutions and standardizing data and model practices.
Define and communicate product vision, mission, and multi-year roadmaps aligned with business priorities and technology transformation goals.
Translate business vision into actionable capabilities that support value, adoption, and associate engagement.
Manage complex, cross-functional, enterprise-scale initiatives across distributed systems and microservices architectures.
Balance business value, technology goals, quality, delivery cost, MVP scope, and timelines.
Establish delivery milestones, timelines, capacity planning, and dependency management.
Promote enterprise-standard analytics consumption and delivery practices across business units.
Guide adoption of TDD, CI/CD, cloud-native architectures, and Agile delivery models including Scrum, Kanban, and SAFe.
Establish and govern code quality metrics, deployment pipelines, integration test coverage, and delivery status visibility.
Advance DevOps practices by automating build, test, and deployment workflows; ensure analytics platforms have observability, logging, and monitoring capabilities.
Manage implementation and operation of advanced analytics, data platforms, and SaaS solutions supporting workforce data use cases.
Evaluate and approve analytics, AI, and data science platforms, including vendor-delivered and managed services.
Partner with cybersecurity, risk, compliance, data architecture, and Centers of Excellence teams to ensure secure and compliant handling of associate and workforce data.
Manage vendor relationships, contract negotiations, SLA governance, escalations, and third-party platform oversight.
Ensure execution of third-party product upgrades, patching schedules, license renewals, and enterprise platform rollouts.