Novartis

Overview

This role leads the design and delivery of enterprise data platforms and AI-ready solutions using AWS, Databricks, Snowflake, Spark, and Python.

About Novartis

Our purpose is to reimagine medicine to improve and extend people’s lives and our vision is to become the most valued and trusted medicines company in the world.

Requirements & Eligibility

• Bachelor's or Master's degree in Computer Science, Software Engineering, Data Engineering, Information Technology, or a related discipline, or equivalent practical experience.
• 12+ years of experience in software engineering and/or data engineering with proven delivery of large-scale production data platforms.
• Advanced hands-on expertise in Python, SQL, data modeling, ETL/ELT, CDC, batch, streaming, and event-driven architectures.
• Strong experience with Apache Spark (PySpark), distributed data processing, performance tuning, and troubleshooting.
• Proven hands-on experience with Databricks, Delta Lake, and Snowflake in enterprise-scale environments.
• Deep knowledge of AWS cloud services, cloud-native data architectures, security, observability, Infrastructure as Code, and automation.

Key Responsibilities

• Design, build, and optimize scalable batch, streaming, CDC, and event-driven data pipelines using modern data engineering best practices and production-grade Python and SQL.
• Architect secure, resilient, high-performance data platforms and translate business and technical requirements into scalable cloud-based solutions.
• Lead hands-on development and optimization of Databricks, Apache Spark, Delta Lake, and Lakehouse solutions, ensuring reliability, performance, and cost efficiency.
• Design and implement Snowflake data engineering solutions, including ingestion, transformation, governance, orchestration, and secure data consumption patterns.
• Engineer enterprise data platforms on AWS, leveraging cloud-native services to deliver secure, automated, observable, and highly available data solutions.
• Develop reusable integration frameworks, APIs, CDC processes, and connectivity patterns across enterprise applications, SaaS platforms, governance tools, ERP, and CRM systems.
• Build AI-ready data foundations, including RAG pipelines, vector retrieval, context engineering, and agentic workflows, enabling enterprise AI and GenAI use cases.