Senior Data Security Engineer

Bank of America•Washington, DC

1d•$128,000 - $182,300

About The Position

At Bank of America, the Senior Data Security Engineer leads complex efforts to develop and deliver data solutions that advance cybersecurity technology and business goals. The role designs and delivers code for integrating, cleaning, transforming, and controlling data in operational and analytics systems. It guides stakeholders, Product Owners, and Software Engineers through solution design and execution, and provides technical leadership informed by experience across complex data solutions, data flows, and systems. Knowledge of the cybersecurity domain is expected. The engineer builds and optimizes data pipelines, architectures, and transformation processes; manages complex datasets and data quality; and partners with development teams to deliver reliable, scalable solutions. The position also supports testing and troubleshooting, documents operational procedures, ensures adherence to data management and release standards, leads experiments and proofs of concept, validates analytical solutions, and mentors junior engineers. Bank of America describes its culture as inclusive and supportive of teammates’ physical, emotional, and financial wellness. It maintains an in-office culture supporting collaboration, engagement, and career development, with role-specific flexibility based on responsibilities and business needs. The position is listed for Denver, Colorado; Washington, DC; or Chicago, Illinois. It is a first-shift role with 40 hours per week. The annual salary range is $128,000–$182,300, with offers based on experience, education, and skill set. The role is eligible for an annual discretionary incentive plan and benefits, including paid time off.

Requirements

Proven experience providing technology leadership and guiding junior engineers toward project completion and delivery.
Experience in the cybersecurity domain, preferably vulnerability data management.
At least 8 years of hands-on data engineering or software development experience.
Ability to move between technology leadership and hands-on coding as required.
Experience with big data processing systems such as Hadoop or Cloudera.
Experience with cloud services and technologies such as AWS or Azure.
Experience with one or more programming languages, such as Python.
Strong SQL and UNIX experience.
Experience with data stores such as HIVE, HBase, and Elastic.
Experience with code versioning systems such as Bitbucket.
Experience with CI/CD pipelines.
Good knowledge of data engineering principles and the SDLC process.
Ability to identify challenges and gaps in requirements early and articulate them to business partners.

Nice To Haves

Proven experience continuously improving technology and the business processes it supports.
Motivation to improve delivery speed without compromising quality.
Experience with data storage, governance, and retention for high-volume datasets.

Responsibilities

Design and engineer complex data solutions across multiple systems, ensuring consistency, scalability, and alignment with business requirements.
Research, prototype, and evaluate technologies and toolsets to improve data capabilities and efficiency.
Build, maintain, and optimize data pipelines, architectures, and transformation processes for analytics and operational needs.
Assemble and manage large, complex datasets while ensuring data quality, metadata management, dependency tracking, and workload optimization.
Partner with development teams to define requirements and implement scalable, reliable data solutions.
Identify improved approaches to technical challenges and challenge existing processes when appropriate.
Support testing, analyze results, troubleshoot issues, and resolve root causes to improve solution quality and performance.
Document deployment, maintenance, support, and operational procedures.
Ensure adherence to data management standards, release processes, CI/CD practices, internal controls, and performance measures.
Lead experimentation and proof-of-concept initiatives and validate analytical solutions.
Mentor junior engineers on engineering best practices and release management processes.