AI-Ready Data Engineer: Build Scalable Pipelines

Bank of America; New York (NY); On-site; USD 140,000 - 190,000; Full time

Benefits offered by this job

401(k)

Job summary

Bank of America is seeking a data engineer to help build and scale the data backbone behind AI initiatives. You will design end-to-end pipelines, implement Data Lake architectures, and collaborate with product owners, data scientists, and business partners.

The role blends hands-on engineering with platform strategy, focusing on reusable data capabilities, governance, and production-grade reliability in a fast-paced enterprise environment.

Qualifications

• Bachelor's degree in a related field required.
• 5+ years of hands-on data engineering experience building production pipelines.
• Advanced SQL, complex queries, large-scale datasets.
• Strong Python and Spark programming, OO design.
• Experience with Airflow or similar schedulers for ETL/ELT workflows.
• Knowledge of Data Lake concepts and lakehouse patterns.
• Ability to work with data governance and stakeholders.

Responsibilities

• Design and deliver end-to-end data pipelines and Data Lake architectures using Python, Spark, SQL.
• Collaborate with Product Owners, Data Scientists, and business partners to productionize AI solutions.
• Define architecture and standards for data and AI across the organization.
• Build production-grade, scalable data pipelines and ensure data quality.
• Own data-to-insight lifecycle and improve data-flow processes.
• Champion data governance and data quality practices with stakeholders.

Skills

SQL expertise

Python

Spark

Airflow

Data governance

Data modeling

Analytical thinking

Communication

Education

Bachelor's degree in Computer Science, MIS, Finance, Statistics, or related field

Tools

Airflow

dbt

Spark

Snowflake

Databricks

Delta Lake

Job description

Bank of America is seeking a data engineer to help build and scale the data backbone behind AI initiatives. You will design end-to-end pipelines, implement Data Lake architectures, and collaborate with product owners, data scientists, and business partners.

The role blends hands-on engineering with platform strategy, focusing on reusable data capabilities, governance, and production-grade reliability in a fast-paced enterprise environment.