Director, Analytics, Data Engineering

Ensure systems are designed for high availability, resilience, and performance at scale.

Lead capacity planning, workload optimization, and cost management initiatives across cloud infrastructure.

Partner with Information Security and Compliance teams to embed governance, access controls, encryption standards, and auditability into architectural frameworks.

Ensure designs support regulatory requirements and enterprise risk management policies.

Work closely with:

• Data Science and Analytics teams to enable advanced modeling and experimentation
• Software Engineering teams to integrate analytics platforms into client-facing solutions
• Product teams to translate business requirements into scalable technical designs

Act as the architectural liaison between business stakeholders and technical teams.

Continuously evaluate emerging technologies, tools, and methodologies to enhance platform capabilities.

Lead proof-of-concept initiatives and guide build-versus-buy decisions.

Mentor senior engineers and managers.

Develop architectural talent within the Data Engineering function.

Influence technical direction without direct ownership of all implementation teams.

Provide architectural roadmaps, risk assessments, and investment recommendations to executive leadership.

Translate complex system design concepts into business-aligned strategy discussions.

Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field required.

Master’s degree preferred.

Minimum of 10+ years of experience in data engineering, distributed systems, or enterprise data architecture.

Minimum of 5+ years designing large-scale cloud-based data platforms.

Demonstrated experience leading architectural strategy across multiple teams or business units.

in media, marketing analytics, or high-volume consumer data environments is a plus.

Preferred certifications:

• Cloud Architecture Certification (AWS, Azure, or GCP)

• TOGAF or equivalent enterprise architecture certification

Deep knowledge of:

• Cloud-native data architectures
• Lakehouse and warehouse platforms
• Distributed computing (e.g., Spark)
• Data orchestration frameworks
• API-driven data integration
• CI/CD for data platforms

Strong understanding of data lifecycle management and system interoperability.

T-SQL, PySpark, Snowflake, cloud-native services (AWS/Azure/GCP), orchestration tools, infrastructure-as-code frameworks.

Ability to design cohesive systems across multiple technologies and teams while balancing performance, cost, risk, and scalability.

Proven ability to influence technical strategy across cross-functional teams without direct reporting authority.

Strong mentorship capabilities.

Exceptional ability to communicate architectural vision, tradeoffs, and risk clearly to technical and executive stakeholders.

Ability to manage multi-year roadmaps, platform migrations, and complex modernization initiatives.

Forward-thinking mindset with the ability to anticipate scaling challenges, emerging technologies, and evolving business requirements.

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