Senior Director of Engineering, Data Platforms
Team: Data Platform
Location: Bengaluru
Commitment: Full Time
Workplace Type: onsite
Key Responsibilities:
- Own and evolve our data engineering strategy, balancing pragmatism with forward-thinking architecture.
- Ensure data quality, availability, and accessibility across the organization, with clear SLAs and accountability.
- Optimize data infrastructure and operations for cost-efficiency, scalability, and ease of use.
- Build and lead a high-performing team of data engineers—instilling a culture of ownership, impact, and curiosity.
- Partner with data science and ML teams to operationalize models and build AI-ready pipelines and platforms.
- Serve as the connective tissue between technical and non-technical stakeholders—translating needs, aligning priorities, and ensuring data drives action.
- Run and continuously improve core data programs, including ETL/ELT, data observability, and metadata governance.
- Stay abreast of emerging tools and practices in modern data stacks, with a keen sense of what fits versus what dazzles.
- Develop and communicate a data engineering roadmap aligned with company goals and future growth.
- Nurture and grow the next line of awesome leaders from your already amazing org! Manage a scale data platform like no other! Prepare to be in a candy store if you like scale and ridiculous data loads.
Skills and Attributes for Success:
- Proven track record of building reliable, scalable, and cost-conscious data infrastructure.
- Deep expertise in modern data tooling (e.g., Snowflake, dbt, Airflow, Kafka, Databricks, etc.) and cloud-native architectures.
- Track record of corralling data in a consumer facing application.
- Experience supporting ML/AI initiatives and integrating data engineering with model development and deployment.
- Strong understanding of data governance, lineage, observability, and security best practices.
- Excellent stakeholder management and communication skills—you’re as comfortable in an exec meeting as in a code review.
- A bias for impact over perfection; you know when to ship, when to invest, and when to say “no.”
Preferred Education & Experience:
- B-Tech or M-Tech in Computer Science or a related technical discipline from a reputed university.
- 14+ years of experience in data engineering, with 3+ years in a leadership capacity.
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