Join EY and build a career tailored to your unique strengths. As a Security Data Engineer, you'll play a crucial role in delivering data engineering services for Information Security technology projects. This individual contributor position involves close collaboration with business and technology stakeholders to architect, develop, and optimize secure, scalable data platforms, ETL processes, and integrations. Your work will enhance reporting, analytics, governance, and operational decision-making capabilities, contributing to a robust cybersecurity posture.

Collaborate closely with diverse business units to thoroughly understand and document complex data needs, translating them into clear technical specifications.

Develop and maintain comprehensive documentation, including data models (conceptual, logical, physical), data dictionaries, and data flow diagrams. These are essential for cybersecurity reporting, risk analytics, compliance monitoring, vulnerability management, identity governance, and security operations.

Employ agile methodologies to design data models incrementally, aligning with sprint objectives and avoiding premature complexity.

Implement robust data lineage and metadata management practices to improve data governance and audibility.

Lead data profiling and quality assessments, supporting the deployment of data validation and business rules.

Drive modernization, automation, and continuous enhancement throughout the data engineering lifecycle.

Support leadership in defining data strategy and ensuring alignment with overarching organizational goals.

A strong foundation in SQL and T-SQL is essential for querying, manipulating, and managing relational databases.

Proficiency is required across various data storage solutions including Databricks, Azure Blob Storage, Dataverse, and Microsoft Graph.

Demonstrated expertise in designing, documenting, and implementing ETL (Extract, Transform, Load) processes is critical.

Expertise in ER/Dimensional Modeling techniques, including star and snowflake schemas for both data warehouse and OLTP environments, is necessary.

Hands-on experience with industry-standard database modeling tools such as ER/Studio and Visio is expected.

A solid understanding of data governance principles, encompassing data quality, metadata management, and data security, is important.

Experience leveraging AI tools in modeling practices and synthetic data generation is a significant advantage.

A minimum of 5 years of experience in the technical skills listed above is required.

Business acumen includes effective partnerships with architects, engineers, product owners, and project teams, translating data models into functional database tables. You should be adept at working with senior business and technology stakeholders to align requirements, set priorities, and achieve delivery outcomes. Strong consulting, communication, collaboration, and problem-solving skills within a matrixed environment are vital.