Principal Engineer - Channels Data Platforms and Analytics

Job Description

At Bank of America, we are guided by a common purpose to help make financial lives better through the power of every connection. We do this by driving Responsible Growth and delivering for our clients, teammates, communities and shareholders every day.

Being a Great Place to Work is core to how we drive Responsible Growth. This includes our commitment to being an inclusive workplace, attracting and developing exceptional talent, supporting our teammates’ physical, emotional, and financial wellness, recognizing and rewarding performance, and how we make an impact in the communities we serve.

Bank of America is committed to an in-office culture with specific requirements for office-based attendance and which allows for an appropriate level of flexibility for our teammates and businesses based on role‑specific considerations.

At Bank of America, you can build a successful career with opportunities to learn, grow, and make an impact. Join us!

The Principal Engineer serves as the architectural authority for Channels Data Platforms and Analytics. This leader defines the target state architecture, drives modernization from legacy technologies to cloud native platforms, and ensures security, resiliency, performance, and compliance across the data and analytics ecosystem. The role provides strategic and technical oversight for the Payment Tracker and guides the transformation of large scale engineering teams and platforms, including mainframe based reporting systems.

The Principal Engineer will lead the architecture, modernization, and transformation of the Channels Data Platform, which today includes on prem data warehouses, Informatica ETL, Hadoop ecosystems, mission critical mainframe processing, and legacy reporting workloads. The role will evolve this platform toward a cloud native, scalable, resilient architecture leveraging Azure, Databricks, Snowflake, Kafka, APIs/microservices, and modern engineering patterns.

• Define and communicate the solution intent and architectural vision across Business, Data, Application, and Technology domains.
• Build and maintain a multi-year modernization roadmap for Channels Data Platforms, including mainframe, Hadoop, ETL, and analytics components.
• Establish target operating models, architectural patterns, and guardrails to support evolving capabilities and regulatory expectations.
• Drive enterprise alignment by collaborating with Enterprise Architecture, Platform Architecture, Information Governance, Risk, and Product.
• Lead transformation from Informatica, on prem databases, Hadoop, and mainframe based reporting into cloud native architectures (Azure, Databricks/Delta, Snowflake, Event Hubs/Kafka).
• Define migration paths for mainframe jobs, batch interfaces, and legacy ledger/reporting logic into scalable modern architectures.
• Implement CI/CD for data, Infrastructure as Code (Terraform/Bicep), observability (logs/metrics/traces), and cost/performance governance.
• Ensure platform resiliency, scalability, and operational readiness (HA/DR, autoscaling, performance tuning, traffic management).
• Provide end-to-end architectural oversight for Payment Tracker, including UI, APIs/microservices, data models, analytics layers, and dashboards.
• Guide design for real-time and near real-time insights, data marts, and advanced analytics embedded in Payment Tracker experiences.
• Ensure adherence to NFRs (performance, security, maintainability, reliability) and integrated monitoring/alerting for application and data services.
• Lead design and code reviews; resolve architectural blockers; ensure compliance with the Bank’s Risk Framework, data privacy, entitlements, masking, and audit expectations.
• Partner with delivery teams to translate strategy into actionable technical backlogs and architecture epics.
• Drive the creation of reusable accelerators, libraries, templates, migration playbooks, and modernization frameworks.
• Mentor engineers and elevate engineering excellence across Channels Technology.
• Own R&D for the data and analytics portfolio: evaluate emerging tools, run POCs/Pilots, benchmark performance, publish decision records, and set technology adoption standards.