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.

Technical Architectural Leadership

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

Delivery Execution Ownership:

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

Cross Functional Stakeholder Collaboration:

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

Platform Economics Business Impact:

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.

Technical Expertise (Depth + Breadth):

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

Leadership Communication:

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

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