Head of Data Engineering

Our client is seeking a Head of Data Engineering to lead end-to-end delivery of data engineering this role you will architect and scale the core data infrastructure that powers their business from data lakes and customer data platforms to AI-enabled analytics products. Youll play a pivotal role in building the foundational systems and data products that drive decision-making across editorial subscriptions advertising and product teams.

This is a high-impact opportunity to lead strategic projects collaborate directly with senior leadership and shape the data backbone of a core media business.

Key Responsibilities

Data Infrastructure & Architecture

• Design build and scale data pipelines and lakehouse architectures supporting audience product and commercial analytics.
• Own the data lake ecosystem defining standards for ingestion storage transformation and access across structured and unstructured data.
• Evolve the clients data stack by evaluating and implementing tools that improve scalability performance and developer experience.
• Build and maintain a centralized feature registry serving as the single source of truth for feature cataloging lineage ownership and SLAs with strong discovery and documentation for Data Science and ML teams.

Data Products & Platforms

• Develop and own core data products including the customer data platform audience intelligence and other AI-powered analytics tools. Ensure they are production-grade reliable and well-documented.
• Build and maintain robust data models that support analytics reporting and ML use cases across multiple business lines.

Governance & Data Quality

• Establish and champion data quality standards governance frameworks and observability practices to ensure organization-wide trust in data.

AI & Advanced Analytics

• Partner with Data Scientists ML Engineers and Product teams to design and deploy AI-driven solutions that grow and engage audiences.
• Translate complex business requirements into scoped production-ready data solutions that operate reliably at scale.

Capability Building

• Stay current on developments in data engineering and AI assessing practical applications for the clients media context.
• Contribute to a culture of technical excellence knowledge sharing and continuous improvement across the data organization.

Candidate Profile

Education

• Masters or PhD in Computer Science Computer Engineering Data Engineering or a related quantitative field preferred.
• Candidates without an advanced degree but with equivalent depth demonstrated through professional track record and impactful work are strongly encouraged.

Technical Experience

Data Products & Platforms

• 15 years designing and building data products such as CDPs audience analytics platforms or personalization/recommendation systems.
• Proven delivery of reliable well-documented data products in production.
• Experience with AI-powered solutions and ML-integrated pipelines highly regarded.

Data Infrastructure & Architecture

• Hands-on experience architecting and maintaining data lake/lakehouse ecosystems with clear standards for the full data lifecycle.
• Demonstrated success building and operating low-latency large-scale batch and streaming pipelines for analytics and ML.
• Experience designing and running centralized feature stores with emphasis on training/serving consistency and reusability.
• Strong proficiency with large-scale data processing frameworks in production.

Analytics Engineering

• Experience with data modeling transformation layer design and documentation standards.
• Strong grasp of data warehousing dimensional modeling and modern lakehouse architectures.

Core Engineering Skills

• Deep proficiency in SQL Apache Spark and Python or Scala.
• Solid understanding of data governance quality frameworks and observability tooling.

Collaboration & Communication

• Excellent ability to translate complex technical concepts for technical and non-technical stakeholders across product editorial and commercial teams.
• Comfortable in cross-functional fast-moving environments where priorities shift.