Key Responsibilities

Engineering Leadership & People Management

·       Build, hire, coach, and develop a high-performing engineering team; own onboarding, performance management, and career growth.

·       Set clear goals and expectations; foster ownership, accountability, collaboration, and continuous learning.

·       Develop technical leaders through feedback, mentoring, design reviews, and knowledge sharing.

Data Platform Architecture & Technical Excellence

·       Lead the evolution of scalable, reliable, secure, and cost-effective data pipelines and cloud data platforms.

·       Provide direction across ingestion, CDC, ETL/ELT, orchestration, modeling, storage, and serving layers using Snowflake, AWS, Kafka, dbt, and Airflow.

·       Partner with architects and Staff Engineers on design reviews, trade-offs, modernization, technical debt, and batch/near-real-time processing.

·       Establish standards for code quality, automated testing, observability, maintainability, and AI-ready data services.

Execution, Delivery & Operational Excellence

·       Own engineering execution, capacity planning, dependencies, risks, and delivery commitments while partnering with Product and technical leaders on roadmap planning and prioritization.

·       Champion DataOps, CI/CD, infrastructure-as-code, automated testing, and safe deployment practices.

·       Own pipeline reliability, incident response, root cause analysis, and prevention; track freshness, quality, availability, cost, and delivery predictability.

Data Quality, Governance & Security

·       Drive data quality, reconciliation, validation, lineage, and monitoring across the platform.

·       Partner with governance, architecture, and security teams on HIPAA compliance, least-privilege access, auditability, and appropriate data protection.

·       Enable trusted, reusable models and data products for self-service analytics and AI applications.

AI-Native Engineering & Innovation

·       Champion responsible AI-native development for coding, testing, documentation, troubleshooting, and reviews with human validation.

·       Partner with Analytics, Data Science, and ML Engineering on AI-ready datasets and reusable data services.

·       Evaluate technologies and automation opportunities that improve engineering productivity and platform efficiency.

Cross-Functional Collaboration

·       Partner with Product, Engineering, Analytics, AI/ML, Security, and business stakeholders to translate needs into solutions.

·       Operate as an integrated member of a globally distributed Data & Analytics team, fostering collaboration, shared ownership, and alignment while clearly communicating progress, technical trade-offs, risks, and decisions.

Required Qualifications

·       Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field, or equivalent practical experience.

·       10+ years in software or data engineering, including large-scale cloud data platforms; 3+ years of direct engineering people management.

·       Strong hands-on SQL and Python; experience with Snowflake, AWS, ETL/ELT, data modeling, distributed systems, and batch/stream processing.

·       Experience with Airflow, dbt, CI/CD, monitoring, automated testing, and infrastructure-as-code; familiarity with Kafka and CDC.

·       Proven architectural judgment, production troubleshooting, engineering delivery leadership, and stakeholder communication.

·       Understanding of security, governance, and reliability in sensitive data environments; ability to remain technically engaged while delegating effectively.

Preferred Qualifications

·       Healthcare technology, SaaS, or regulated-industry experience.

·       Snowflake optimization, cost management, dimensional modeling, semantic layers, and self-service analytics.

·       AI/ML data infrastructure, and practical AI-native SDLC adoption.

·       Experience leading distributed teams across India and U.S. time zones

Business Impact & Success Measures

·       Platform reliability, freshness, incident prevention, and recovery time.

·       Predictable delivery, improved engineering productivity, and reduced technical bottlenecks.

·       Data quality, reconciliation, observability, governance, and trusted analytics.

·       Cloud cost efficiency, scalability, automation, and responsible AI-assisted development.

·       Team growth, engagement, clear ownership, and timely delivery of customer and AI-enabling data products.

Leadership Expectations

The successful candidate is both a technical leader and a people manager: able to dive into difficult problems, empower engineers to own solutions, make pragmatic trade-offs, and remain accountable for team and operational outcomes. The manager owns people, execution, and reliability while partnering with Staff Engineers on deep technical direction

Equal Opportunity Employer
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