Director of Data Engineering

Role: Director of Engineering, Data

Location: Bangalore (Kormangala)

Work Model: Hybrid

Qualification:

• 10+ years of software engineering experience, including significant time leading large-scale data systems and teams.
• Deep domain expertise in batch & real-time data processing systems.
• Understand applicability, pros & cons & effectiveness of data engineering design patterns & concepts such as Data Lakehouse, Data Table Format, Zero-Copy Data Sharing, Cleanroom, Real-Time vs Batch Data Processing, High throughput distributed system APIs
• Possess experience working on data technologies equivalent to:

Snowflake, Redshift, BigQuery

Iceberg, Hudi, Delta

EMR, Airflow, Spark

Aerospike, Scylla, DynamoDB

• Strong understanding of cloud data architectures (AWS, Snowflake, etc.).
• Track record of innovation and driving measurable outcomes, including AI-driven or data-driven product capabilities.
• Exceptional communication skills

Key Responsibilities:

• Lead the Prospect Audiences team, consisting of 10 engineers, fostering a culture of innovation, collaboration, and operational excellence.
• Guide the team to deliver roadmap work items on time, of high quality, within the boundaries of the organization's processes and guidelines.
• Collaborate with product and business teams to shape the future and long-range roadmap for the team
• Collaborate with DevEx & AI Enablement teams to drive adoption of AI-native SDLC in the agentic software development paradigm, as applicable to the technologies used by the team.
• Partner with architects to build a world-class data ingestion and processing architecture & be at the forefront of technological innovation and continue to challenge yourself to deliver better outcomes for the customers.
• Recruit, mentor, and develop top talent within your team and the data layer in general.
• Own the system end to end, including production deployments, monitoring, distributed tracing, defining SLOs and thresholds for alerting, and managing on-call support for production incidents.
• Monitor cost profile of the owned systems, participate in cost optimization initiatives, and execute cost optimization projects to keep the overall cost footprint as lean as possible.
• Own how the team ensures reliable, testable pipelines and datasets - quality checks, lineage/observability of critical tables, and clear contracts/SLAs (or handshakes) between data producers and downstream consumers (including when things break).