Senior Data Engineer

Bank of America•Charlotte, NC

17h•Onsite

About The Position

At Bank of America, the Senior Data Engineer drives enterprise-wide data engineering capabilities and complex data solutions. The role leads the design, development, and implementation of enterprise-scale data platforms and integration solutions, working with project management and development teams to define outcomes, meet business goals, and integrate solutions across systems and domains. It provides technical leadership through design reviews, mentoring, and coaching, and ensures solutions meet functional, non-functional, and compliance requirements. The position works with large operational and analytical data systems, building and maintaining data pipelines and transformation processes for real-time, near-real-time, and batch processing. It coordinates with deployment and support teams to release projects into testing and production, works across time zones and teams, and may lead multiple projects with competing deadlines. The role requires Treasury Payments and Deposits domain knowledge and operates with minimal supervision. The position is a first-shift role in the United States of America, with 40 hours per week. Bank of America describes an in-office culture, with flexibility based on role-specific responsibilities and business needs.

Requirements

10 or more years of data engineering experience; the overview also specifies typically 10 or more years of data delivery/integration experience.
Hands-on experience with Scala, Spark, and Kafka Streaming.
Strong understanding of Hadoop components, architecture, and toolsets, including Spark and Scala, Informatica, Starburst, and Object Storage in enterprise data platforms.
Experience architecting, developing, and implementing solutions using real-time streaming technologies.
Experience designing, developing, and deploying real-time, near-real-time, and batch data integration solutions.
Experience with large-scale analytical platforms, metadata-driven architectures, and distributed data processing frameworks is listed as preferred in the required qualifications section.
Ability to design and implement cloud-native data platforms using Object Storage, Data Lakehouse architecture, distributed query engines, and streaming technologies.
Knowledge of performance tuning.
Experience with Exadata and Oracle SQL tuning.
Experience with Starburst and Object Storage.
Hands-on experience with UNIX shell scripting.
Working experience with scheduling tools.
Knowledge of Hadoop Spark and Spark Streaming best practices and standards, and standard software development lifecycle experience.
Ability to ensure software meets functional, non-functional, and compliance requirements and integrates successfully into the overall application or system with robust, well-tested interfaces.
Development experience using Agile processes.
Knowledge of Treasury Payments and Deposits domains.
Solid quantitative, analytical, process development, facilitation, and organizational skills.
Strong communication, presentation, interpersonal, software development, and work management skills.
Ability to quickly grasp technology concepts and work under minimal supervision.

Nice To Haves

Experience with CI/CD build and deployment tools.
API development experience.
Financial services or banking experience.
Experience in distributed computing.
Experience with open table formats such as Apache Iceberg, Delta Lake, or equivalent technologies.
Experience with enterprise metadata management, data lineage, governance, and data quality frameworks.

Responsibilities

Lead requirement analysis, high-level and low-level design, development, and implementation of enterprise-scale data platforms and integration solutions.
Conduct design reviews, mentor and coach development teams, and produce comprehensive, flexible solution designs.
Ensure complex feature designs and engineering approaches are consistent with the broader portfolio solution.
Assemble large, complex data sets that meet functional and non-functional requirements across multiple systems.
Maintain, improve, clean, and manipulate data for operational and analytical systems; build processes for data transformation, structures, metadata, data quality controls, dependencies, and workload management.
Communicate information required for deployment, maintenance, and support of business functionality.
Use multiple architectural components to design and develop client requirements, collaborating with development teams to ensure data architecture is feasible to implement.
Define and build data pipelines that support data-informed decision-making and adhere to release processes and risk management routines.
Contribute to integration, regression, and performance test suites; analyze test reports, identify issues and errors, and lead triage of underlying causes.
Identify gaps in adherence to data management standards and work with partners on plans to close them.
Lead concept testing and research to prototype toolsets and improve existing processes.
Mentor Data Engineers on continuous integration and continuous delivery events, and define key performance indicators and internal controls.
Coordinate with deployment and support teams to ensure projects are deployed successfully into testing and production environments.
Lead multiple projects concurrently, including projects with competing deadlines, and work with teams in different time zones.