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…