Director of Data Engineering and platforms

Key Responsibilities:

• People & Team Leadership: Lead, hire, mentor, and grow high-performing data platform engineering teams while fostering a culture of ownership, accountability, and engineering excellence.
• Technical & Architectural Stewardship: Provide technical direction and architectural oversight for large-scale, real-time data platforms supporting media streaming, analytics, and AI/ML workloads.
• Execution & Delivery Ownership: Own end-to-end delivery of platform initiatives, ensuring predictable execution, high system reliability, and operational excellence at scale.
• Cross-Functional Partnership: Partner with Product, AI/ML, Analytics, and Business stakeholders to translate business goals into scalable platform outcomes.
• Platform Economics & Impact: Drive cost efficiency, scalability, and data governance while improving time-to-insight and business decision-making.

• Provide technical direction and architectural oversight for large‑scale data systems
• Review and guide system designs, trade‑offs, and long‑term platform decisions
• Ensure data platforms support AI/ML workloads, MCP services, and real‑time personalization
• Balance hands‑on technical involvement with effective delegation
• Champion best practices in distributed systems, reliability, security, and data governance

• Own end‑to‑end delivery for platform initiatives—from planning to production
• Translate business and product requirements into clear technical roadmaps and milestones
• Ensure predictable execution through sprint planning, prioritization, and dependency management
• Drive operational excellence: monitoring, incident management, SLAs, and post‑mortems
• Continuously improve developer productivity, system stability, and release velocity

• Partner closely with Product, Analytics, AI/ML, Media Tech, and Business teams
• Act as the primary technical and delivery interface for senior stakeholders
• Influence platform strategy through data‑driven insights and clear communication
• Ensure data is trusted, discoverable, and accessible across the organization

• Own cost efficiency and unit economics across ingestion, storage, and processing layers
• Drive optimization initiatives for cloud infrastructure and tooling
• Ensure platforms reduce time‑to‑insight and improve business decision‑making
• Balance innovation with operational sustainability at scale

Skills and attributes for success:

• Leadership & Management: Proven ability to lead and scale senior engineering teams, coach talent, and manage performance in fast-paced environments.
• Technical Depth: Strong understanding of distributed data systems, streaming architectures, cloud platforms, and data foundations for AI/ML.
• Execution Excellence: Demonstrated ownership of complex platform deliveries with a strong bias toward reliability, observability, and continuous improvement.
• Systems Thinking: Ability to evaluate architectural trade-offs and guide teams toward sustainable, long-term solutions.
• Communication & Influence: Clear communicator who can influence technical and non-technical stakeholders and drive alignment across teams.

• Strong understanding of real‑time and batch data architectures
• Hands‑on experience with distributed processing frameworks (Spark, Flink, or equivalent)
• Deep familiarity with Kafka/Kinesis, event‑driven systems, and streaming pipelines
• Cloud expertise with AWS (S3, EMR, Kinesis, Glue, Redshift, etc.) or equivalent
• Strong grasp of data lakes, warehousing, and modern table formats (Iceberg, Delta, Hudi)
• Experience supporting AI/ML data pipelines, feature stores, and training workflows
• Solid grounding in databases, data modeling, and SQL

• Ability to communicate complex technical concepts to non‑technical stakeholders
• Strong decision‑making skills with a bias for action and long‑term thinking
• Experience influencing architecture and roadmap beyond your immediate team
• Passion for building inclusive, diverse, and high‑impact teams

Preferred Education & Experience:

• B-Tech or M-Tech in Computer Science or a related technical discipline from a reputed university.
• 10+ years of experience in data engineering, with 3+ years in a leadership capacity.
• Experience leading multi‑team or platform‑wide initiatives
• Prior exposure to media streaming, ad‑tech, personalization, or AI platforms
• Track record of scaling teams and systems during periods of rapid growth

If you would like more information about how your data is processed, please contact us.