AI Data Engineer – Comcast Global Audit

Comcast Comcast · Media · Philadelphia, TX - Austin, PA

AI Data Engineer responsible for building and maintaining a modern audit data platform supporting advanced data analytics, automation, and artificial intelligence. Focuses on designing scalable AI and automation solutions, resilient data pipelines with emphasis on automation, quality, governance, and observability. Translates business requirements into production-grade pipelines and AI/automation solutions.

What you'd actually do

  1. Clarifying and designing solution requirements and architecture for AI / Automation working with auditors by understanding their processes and seeking to identify opportunities to streamline
  2. Turning fragmented, manual data inputs into production-grade pipelines that support audit engagements across Comcast’s business units
  3. Supporting and providing feedback in the design of scalable data solutions that support audit objectives
  4. Overseeing other team members in driving consistency across data ecosystem
  5. Proactively identifying improvement areas that can lead to increased standardization and automation, leveraging artificial intelligence where relevant, strategically aligning with audit senior leaders on ways to improve and streamline audit testing.

Skills

Required

  • SQL
  • dbt
  • Snowflake
  • Airflow
  • Tableau
  • AWS
  • MS SQL Server
  • Oracle BI
  • Teradata
  • Databricks
  • Alteryx
  • Spark
  • Python
  • R
  • ETL
  • data analysis
  • project management
  • system thinking
  • technical decision making
  • CI/CD pipelines
  • GitHub Actions
  • infrastructure-as-code
  • version control
  • code reviews
  • documentation
  • testing
  • observability
  • logging
  • alerting
  • traceability
  • data integration
  • APIs
  • SFTP
  • databases
  • root-cause analysis
  • data security
  • data classification
  • compliance analysis
  • US GAAP
  • legal/governmental requirements analysis
  • process flow design
  • prototyping
  • requirements documentation
  • workpaper preparation
  • executive presentation

Nice to have

  • MS CoPilot
  • GitHub CoPilot
  • Azure
  • ChatGPT

What the JD emphasized

  • production-grade pipelines
  • scalable data solutions
  • automation
  • quality
  • governance
  • observability
  • AI / Automation solutions
  • resilient, automated data pipelines
  • modular, well-documented transformation logic
  • end-to-end pipeline monitoring: logging, alerting, and traceability
  • CI/CD pipelines
  • data integration across diverse sources
  • robust engineering solutions
  • code reviews
  • team-wide engineering standards
  • incremental benefits
  • tangible deliverables
  • short-term audit needs
  • long-term architectural improvements
  • technical debt
  • manual effort
  • operational rigor
  • testable, observable, well-documented code
  • collaborative planning
  • sprint cycles
  • data security
  • company policies regarding data classification
  • compliance with established policies/procedures
  • best practices
  • US GAAP
  • legal/governmental requirements
  • ETL
  • data analysis activities
  • AI technologies
  • process flows
  • prototypes
  • requirements documentation
  • support process steps
  • test procedures
  • data deliverables are accurate and sustainable
  • decision-making throughout the lifecycle
  • creating and enhancing data solutions
  • analysis workpapers
  • root-cause (as applicable)
  • work performed
  • findings
  • recommendations
  • graphical reports
  • executive presentations
  • summarize analysis and findings
  • reviews team workpapers, presentations, and other documentation
  • clear, complete, and well-organized
  • independent judgment and discretion
  • managing, scheduling, monitoring, and maintaining automated processes in production
  • department objectives

Other signals

  • AI/Automation solutions
  • data pipelines
  • automation
  • quality
  • governance
  • observability