Senior Data Engineer

Role overview

Responsibilities

• Assemble and maintain complex data sets and build transformation processes, metadata, quality controls, and workload management for operational and analytics systems.
• Build data pipelines, support data-informed decisions, and collaborate with development teams on feasible data architectures.
• Contribute to integration, regression, and performance testing; analyze issues and lead root-cause triage.
• Identify standards gaps, plan remediation, prototype tools, mentor data engineers, and define performance indicators and internal controls.
• Define standards and scalable controls for organizing, governing, indexing, validating, and retrieving AI-ready data and knowledge assets.
• Partner with network product and service experts to improve model grounding and decision quality.
• Establish ownership, stewardship, lineage, freshness, governance, and quality accountability across relevant domains.

Required qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, Mathematics, Statistics, or a related field.
• 7+ years of data engineering experience, including leadership in enterprise data platforms.
• Advanced proficiency in SQL and Python.
• Expertise in data integration, pipeline development, large-scale processing, data quality, automated validation, modeling, metadata, lifecycle management, governance, lineage, and controls.
• Strong understanding of AI/ML data requirements and data readiness.
• Ability to design scalable, high-performance analytics platform solutions.

Desired qualifications

• A master’s degree in a technical or data-related field is a plus.
• Desired experience includes AI-ready data ecosystems, enterprise data quality programs, governance frameworks, DataOps or MLOps, and hybrid cloud/on-premises architectures.
• Regulated-industry experience and a record of driving data strategy or platform modernization are desired.

Listed skills

• Analytical thinking, application development, data management, risk management, solution design, agile practices, architecture, collaboration, decision making, DevOps practices, data quality management, Python, SQL, and critical thinking.

Schedule

• First shift in the United States of America; 40 hours per week.

Workplace

• The employer describes an in-office culture with role-specific office attendance requirements and some flexibility based on role and business needs.