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