Senior Director of Data Platform Engineering

Comcast•Philadelphia, PA

11h•$198,510 - $297,766•Hybrid

About The Position

Comcast is seeking a Senior Director of Data Platform Engineering to lead and expand our Data Platforms Engineering team. This team is the foundation of our Data, AI and Machine Learning (ML) infrastructure, providing the critical platforms and tools that empower our Data Engineers, Data Scientists and ML Engineers to efficiently build, test, and deploy data workloads, pipelines and models at scale. The ideal candidate should have strong expertise in data storage, compute, data fabric, unified data catalogs, data products, workflow orchestration, feature stores for AI, ML ops, data observability, and supporting data for AI models with overall ML / AI driven engineering development approach. As the Senior Director of Platform, you will set the strategic vision, guide technical execution, data and platform architecture, SDLC patterns and contribute as a hands-on engineer to ensure scalable and practical solutions. Your leadership in this role will help drive data innovations that enhance the customer experience across Comcast’s products and services

Requirements

15+ years of experience in Data, backend, streaming, API management, data engineering, or platform engineering, with ideally 4+ years in ML/AI.
5+ years of engineering management experience, ideally in data platform role.
Deep technical understanding of modern data architecture (lakehouse, streaming, data mesh, serverless pipelines, iceberg, open table formats)
Background in broadband or telecommunications would be highly preferred.
Strong leadership in matrixed organizations, skilled in driving alignment across engineering, AI, and business units
Expertise in Data and ML Ops and data infrastructure development.
Strong understanding of cloud-native infrastructure and scalable ML systems.
Proven ability to align data and AI platform strategy with business and product objectives.
Passion for mentorship, coaching, and team development.
Excellent communication skills, with the ability to collaborate across technical and non-technical teams.
Strong ability to evaluate build vs. buy decisions for ML tooling and infrastructure.
Experience with cost optimization and platform observability frameworks.

Nice To Haves

Contributions to open-source or thought leadership in data/AI engineering communities.
Experience with AWS S3, MinIO, Argo, Kafka, Kubernetes, Terraform, Databricks, Flink, Ray, or other data ML Ops tooling.
Familiarity with governance models (data cataloging, lineage, access management).
Proven success designing and scaling enterprise-grade data platforms in cloud or hybrid environments (AWS, Azure, or GCP).

Responsibilities

Drive modernization of legacy data systems by defining strategies to transition from traditional architectures to modern, cloud-native, and AI-driven platforms while maintaining business continuity.
Accelerate real-time data delivery to meet urgent business needs by implementing streaming and low-latency data solutions that enable faster decision-making.
Champion agility and speed in platform development by fostering a culture of rapid iteration, automation, and continuous improvement across the SDLC.
Adopt a platform-as-a-product mindset, ensuring scalability, usability, and cost efficiency while balancing innovation with financial responsibility.
Lead engineering initiatives that integrate multi-cloud and AI infrastructure, optimizing for performance, cost, and security.
Collaborate cross-functionally to align platform capabilities with evolving business priorities, competitive pressures, and cultural transformation goals.
Promote cutting-edge technology adoption and open new horizons for the organization by leveraging modern tools and frameworks (e.g., Snowflake, Kafka, Kubernetes, AI/ML stacks).
Instill engineering excellence through robust design standards, code quality, and reliability practices, ensuring the platform can scale with business demands.
Drive cultural and technological change by inspiring teams to embrace innovation, experimentation, and a forward-thinking approach to data and AI.

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