Director of Engineering

About Hevo

Hevo is an automated unified data platform that helps organizations effortlessly move and sync data across 150+ sources to modern cloud data warehouses like Snowflake, BigQuery, and Redshift - all with no-code and real-time capabilities. We’re on a mission to empower teams to make faster, data-driven decisions by simplifying data integration at scale.

Key Responsibilities

Engineering Leadership & Strategy

• Define the long-term technical vision and engineering roadmap in alignment with Hevo’s product and business strategy.
• Build and scale engineering teams, fostering a high-performance culture rooted in ownership and innovation.
• Serve as a thought partner to the CXO, HoE and leadership team on product and platform evolution.

Architecture & Technical Direction

• Lead architectural decisions for a large-scale, real-time data platform ensuring scalability, performance, and reliability.
• Drive modernization across ingestion, transformation, orchestration, and metadata systems.
• Evaluate emerging technologies and adopt those that advance the platform’s capabilities and efficiency.
• Champion technical excellence and establish best practices across the org.

Delivery & Execution

• Oversee end-to-end product delivery, ensuring timely releases with high quality and customer impact.
• Build robust engineering processes (planning, reviews, incident management) to support rapid scaling.
• Partner closely with product management to align priorities and balance speed with long-term maintainability.
• Drive execution discipline while maintaining a bias for speed and innovation.

People & Organizational Development

• Attract, develop, and retain top engineering talent across multiple domains.
• Build leadership capacity within engineering through mentorship and succession planning.

AI-Native Engineering & Platform Intelligence

• Drive adoption of LLM-powered developer productivity tools to accelerate engineering throughput.
• Leverage AI to improve observability, incident triaging, and root cause analysis across distributed ingestion systems.
• Architect AI-assisted features for customers (e.g., automated connector mapping, transformation suggestions, query optimization).
• Establish responsible AI practices including governance, evaluation frameworks, and model performance monitoring.
• Identify opportunities to reduce operational overhead through automation and AI-driven decision systems.
• Embed AI/ML capabilities into the core platform (e.g., anomaly detection, pipeline health intelligence, intelligent retries, data quality insights).