Sr Data Engineer

Bank of America•Charlotte, NC

4d•Hybrid

About The Position

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 and providing a culture of caring is core to how we drive Responsible Growth. We are intentional about fostering an inclusive workplace where every teammate has the opportunity to succeed, build a career and contribute to our shared success. This includes attracting and developing exceptional talent, recognizing and rewarding performance, and supporting our teammates’ physical, emotional, and financial wellness through affordable, competitive and flexible benefits. We value the unique perspectives individuals bring from all backgrounds and career paths - whether shaped by military service, community college education, or a wide range of work and life experiences. These journeys foster resilience, leadership and innovation, strengthening our workforce and positively impact the communities we serve. Bank of America is committed to an in-office culture that supports collaboration, engagement, and career development. Our approach includes clear in-office expectations, while providing an appropriate level of flexibility based on role-specific responsibilities and business needs. At Bank of America, you can build a successful career with opportunities to learn, grow, and make an impact. Join us! Our team runs a range of data and AI projects across the GPS business. A few examples: A modern, AI-ready data platform and lakehouse that supports analytics, machine learning, and generative AI across the org. AI-powered tools that give sales and product teams fast, reliable answers to product, servicing, and client questions. Models that optimize pricing and foreign-currency conversion to drive real business value. Real-time pipelines that turn raw client and servicing data into insights teams can act on. This is a hybrid role that spans data engineering, AI product enablement, and platform architecture. You'll build and scale the data backbone behind AI at GPS: engineering AI-ready data products, helping shape platform direction, and taking solutions from idea to production. We want someone who enjoys both the hands-on craft of building strong data platforms and the bigger opportunity to influence how the organization delivers AI. Whether you lean engineer or lean strategist, there's room here to do both.

Requirements

Bachelor's degree in Computer Science, Management Information Systems, Finance, Statistics, or a related field required.
5+ years of hands-on Data Engineering experience building and operating production-grade data pipelines and platforms.
Advanced SQL expertise, deep proficiency in writing, optimizing, and tuning complex SQL and stored procedures across large-scale relational and distributed datasets (query performance, window functions, partitioning, and data modeling).
Strong programming background in Python and Spark, with solid Object-Oriented design principles and a focus on reusable, testable, production-quality code.
Modern data pipeline orchestration — hands-on experience with Apache Airflow (or comparable modern schedulers) to build, schedule, and monitor reliable end-to-end ETL/ELT workflows.
Data Lake and modern platform architecture — strong understanding of Data Lake concepts, dimensional modeling, data virtualization, and modern data delivery methodologies (medallion/lakehouse patterns, distributed processing).
Experience delivering Data & AI solutions within large, matrixed organizations, with the ability to quickly assess and adopt the right sourcing and architecture strategy.
Strong quantitative, analytical, and problem-solving skills, paired with critical thinking and creativity.
Ability to build effective relationships with business and technology partners.
Outstanding verbal and written communication skills, with the ability to express complex technical concepts in business terms across all levels of management.

Nice To Haves

Modern data engineering & orchestration tooling such as Airflow, dbt, Spark, and CI/CD for data pipelines (Git-based workflows, automated testing, and deployment).
Cloud and lakehouse platforms experience with modern cloud data warehouses and lakehouse technologies (e.g., Snowflake, Databricks, Delta Lake) alongside distributed storage (HDFS, Hive, Apache Spark).
Advanced SQL & database platforms performance tuning and engineering across RDBMS and Big Data platforms such as Oracle Exadata, SQL Server, and Teradata.
ETL/ELT and streaming technologies Python, Spark, SSIS, shell scripting, and exposure to real-time/streaming frameworks (e.g., Kafka).
BI and data visualization Tableau (Desktop and Server), Power BI, SSRS, and SSAS.
AI/GenAI enablement familiarity with building data foundations that support machine learning and generative AI use cases.
Banking and Global Treasury transactions industry experience a plus.

Responsibilities

Build the data foundation for AI. Design and deliver end-to-end pipelines and Data Lake architecture using Python, Spark, SQL, and modern ETL practices, turning large raw datasets into trusted, AI-ready data products.
Enable AI products through data. Work with Product Owners, Data Scientists, and business partners to move AI and GenAI solutions into production, turning business problems into reusable data capabilities.
Set platform standards. Define the architecture, patterns, and standards for how data and AI get built, deployed, and used across the org, with an eye on reusability, performance, and cost.
Engineer production-grade solutions. Apply Object-Oriented design, solid data platform concepts, and strong ETL practices to build reliable, scalable pipelines, and help shape where the team's data capabilities go next.
Own the data-to-insight lifecycle. Review and improve data-flow processes, understand how data gets consumed, and make sure insights reach the people who use them.
Champion trusted, governed data. Build Data Governance and Quality principles into your work and act as a reliable partner to stakeholders and data consumers.
Drive projects from idea to production. Take new concepts and deliver them across business and Enterprise IT partnerships in a fast-paced environment.
Keep the customer first. Anticipate needs, take initiative, and deliver solutions that work well for internal and external customers.