Head of Data Engineering

Our client is seeking a Head of Data Engineering to lead end-to-end delivery of data engineering initiatives. In 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. You'll 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 client's 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 client's media context. Contribute to a culture of technical excellence, knowledge sharing, and continuous improvement across the data organization. Candidate Profile Education Master's 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. Originally posted on Himalayas