Sr Data Engineer
$125,000–$145,000 year
HybridCharlotte, North Carolina, United States or New York, United States
Job Summary
Design and deliver end-to-end pipelines and Data Lake architecture using Python, Spark, and SQL to build the data foundation for AI across GPS. Work with Product Owners and Data Scientists to move AI and GenAI solutions into production, defining standards for architecture, patterns, and governance while engineering production-grade, scalable solutions. Own the data-to-insight lifecycle by reviewing processes and ensuring insights reach stakeholders, driving projects from idea to production in partnership with Enterprise IT. This hybrid role spans data engineering, AI product enablement, and platform architecture within Bank of America's GPS business.
Required Qualifications
- Bachelor's degree in Computer Science, Management Information Systems, Finance, Statistics, or a related field
- 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
- 1st shift (United States of America)
- 40 hours per week
Desired Qualifications
- 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
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