Director - Data Engineering and platforms
Team: Data Platform
Location: Bengaluru
Commitment: Full Time
Workplace Type: onsite
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.
- 12+ years of experience in data engineering, with 5+ 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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