Position Title: Asc. Dir. Dev. IT Data, Analytics, DS&AI

Location – Hyd-India #LI Hybrid

About the Role

Lead the vision and execution of advanced data engineering capabilities required for Drug Development use cases on enterprise Data and Analytics Platforms. The Development Data Enablement team is responsible for implementing next-generation data pipelines and adopting best-in-class data engineering practices for processing, storing, and delivering analytical and AI/ML-ready data, leveraging technologies such as Databricks, Delta Live Tables, Delta Lake, and cloud-native Lakehouse architectures.

The Associate Director – Data Engineering role ensures robust and scalable data solutions supporting analytics, advanced analytics, and AI workloads in compliance with governance, quality, and operational standards. The role requires close collaboration with data architects, platform teams, data scientists, and other stakeholders to deliver modern, reusable, high-performance datasets

Your responsibilities include but are not limited to

• Directly contribute in the design, implementation and rollout of Data Engineering solutions for Development Domain, including code management, review, testing and performance engineering. 
• Architect and implement scalable, reusable pipelines for batch and streaming data using Delta Live Tables and Databricks workflows.  
• Establish and enforce best practices in Deltalake design and architecture including modular design, schema evolution, and efficient storage optimization.  
• Align to enterprise data management standards to incorporate governance, quality, lineage, and cataloging processes into engineered solutions.  
• Collaborate with Architecture Platform & Data teams to embed enterprise data architecture standards, quality gates, and engineering management approaches.  
• Drive adoption of data reliability engineering principles including CI/CD for data pipelines, automated testing, and monitoring.  Coordinate with vendor engineering teams to ensure the right skills and Databricks expertise are in place and delivering to expectation.  
• Champion cost-optimized data processing strategies for large-scale datasets on cloud infrastructure (AWS/Azure).  Stay ahead of emerging trends in big data/Lakehouse/DeltaLake space and integrate innovations beneficial to Pharma R&D data use cases.  
• Ensure all deliveries are secure, compliant with ISRM/legal/ethical guidelines, and operationally reliable.

What you’ll bring to the role:

• Strong vendor and stakeholder management experience in global matrix organizations.  Familiarity with data management frameworks, data governance & quality tools (e.g., Collibra, Informatica, Reltio).  
• Business Alignment – capable of translating business needs into modern, governed engineering solutions.  Collaboration & Influence – proven ability to work across technical and non-technical stakeholders globally.  
• Data Engineering Leadership – ability to define technical direction, enforce engineering quality, and mentor teams. Continuous Learning – staying up to date with evolving big data and Lakehouse patterns.

• Batch & streaming ingestion from multiple sources including APIs, IoT, and operational databases. Modern data architecture patterns (Medallion architecture, CDC, schema evolution).  
• Unit testing, integration testing, and monitoring for pipelines.  Governance and metadata management integration into engineering workflows.  
• Ensure that system designs adhere to solution architecture design (i.e. high level conceptual design) and are traceable to functional as well as non-functional requirements in projects/enhancements
• Ensure designs produced adhere to architectural roadmap and support the development, execution and operations of solutions. System design standards are defined to improve and sustain standardization of solutions
• On-time delivery of scalable big data pipeline solutions. Increased data reusability and reduced time-to-data for analytics and AI teams. High adoption of governed, high-quality data lake datasets and data products. 
• Stability and performance of production data pipelines. Demonstrated cost savings and performance optimizations in Lakehouse implementations.  Adherence to solution architecture and architecture roadmaps. Adherence to SLAs

Desirable Requirements:

• Bachelor's or Master's University degree in Computer Sciences, or similar

• 12+ years of IT experience with 8+ years in data engineering on large-scale, cloud-based big data platforms.  Hands-on expertise in Databricks, Data Lake, Delta Lake and Lakehouse architecture for large data lifecycle implementations.  
• Hands on and technical Excellence – advanced knowledge of Spark SQL, PySpark, Databricks Workflows, Delta Live Tables
• Strong track record in designing engineering solutions supporting both batch and streaming data. Successfully delivered production-grade data pipelines in AWS/Azure environments with attention to security, compliance, and scalability.  
• Deep experience with CI/CD, code management tools, automated pipeline testing, and performance tuning.  Pharma domain knowledge and ability to align data engineering solutions with R&D and clinical data needs.  

• IT Systems Design, IT Systems Development Management, IT Systems Integration and Knowledge of project management models and techniques. 

Commitment to Diversity & Inclusion:

Novartis embraces diversity, equal opportunity, and inclusion. We are committed to building diverse teams, representative of the patients and communities we serve, and we strive to create an inclusive workplace that cultivates bold innovation through collaboration and empowers our people to unleash their full potential.