Senior Full Stack Data Engineer

Capital Numbers is seeking a senior-level Full Stack Data Engineer to design, build, and scale core enterprise data infrastructure. This role demands an enginee

Roles &

RESPONSIBILITIES

Pipeline & Architecture Engineering: Design, build, and maintain scalable batch and real-time streaming ETL/ELT data pipelines, cloud data platforms, and lakehouse storage. Backend & API Development: Develop robust, production-grade REST APIs, microservices, and event-driven backends to integrate data platforms seamlessly with operational systems. AI & Analytics Enablement: Structure clean data architectures, CDC pipelines, and semantic layers to support operational analytics, feature engineering, and downstream AI/ML workflows. Platform Governance & Performance: Enforce data quality, observability, cost control, and monitoring while resolving production incidents and managing schema evolutions. Infrastructure & Dev Ops: Manage containerized deployments, automated CI/CD pipelines, and cloud infrastructure monitoring across enterprise networking environments. Leadership & Mentorship: Drive engineering best practices, conduct code reviews, unblock team members, and mentor junior engineers across projects. Skills: Data Engineering: Python, SQL, Spark / Py Spark, Airflow, Kafka, Google Pub/Sub, dbt, Snowflake / Big Query / Redshift, Postgre SQL / My SQL, Parquet / Delta Lake / Iceberg Backend & Platform: REST APIs, Fast API / Flask / Node.js, Microservices, Event-Driven Architectures, Message Queues (Rabbit MQ / Active MQ), Async Processing Cloud & Dev Ops: AWS / GCP / Azure, Docker, Kubernetes, CI/CD (Git Hub Actions / Jenkins), Terraform basics, Juniper enterprise networking ecosystems AI/ML Exposure: ML/AI data pipelines, Feature Stores, Vector Databases, LLM / RAG architectures, Real-Time Analytics Soft Skills: Technical Ownership, Production Debugging, Systems Thinking, Mentorship, Executive Communication Qualification & Experience

EDUCATION: B.Tech / B.E. in Computer Science, IT, or equivalent engineering discipline (candidates from premier institutes like IITs, NITs, BITS, IIITs preferred). Track Record: Proven background building production-scale data platforms within product companies, Saa S platforms, high-scale startups, or data-intensive software organizations. Domain Advantage (Preferred): Prior experience in Healthcare Tech, EMR/HMIS systems, clinical data platforms, or healthcare data privacy standards.