Roles & Responsibilities Design and implement scalable, modular, and future-proof data architectures supporting enterprise initiatives Develop enterprise-wide data frameworks for governed, secure, and accessible data across business domains Define data modeling strategies for structured and unstructured data ensuring efficiency and usability across analytics platforms Lead development of high-performance data pipelines for batch and real-time processing, integrating APIs, streaming, transactional systems, and external data platforms Optimize query performance, indexing, caching, and storage to enhance scalability, cost efficiency, and analytics capabilities Establish data interoperability frameworks enabling seamless integration across multiple sources and platforms Drive data governance strategies, embedding security, compliance, access controls, and lineage tracking into enterprise data solutions Implement DataOps best practices including CI/CD for pipelines, automated monitoring, and proactive issue resolution to improve operational efficiency Lead Scaled Agile (SAFe) practicesfacilitate PI Planning, Sprint Planning, and Agile ceremonies ensuring iterative delivery of data capabilities Collaborate with business stakeholders, product teams, and technology leaders to align data architecture with organizational goals Act as trusted advisor on emerging data technologies and trends to maintain competitive advantage and long-term scalability Must-Have Skills Experience in data architecture, enterprise data management, and cloud-based analytics solutions Expertise in Biotech/Pharma domain with strong data strategy problem-solving Proficiency in Databricks, cloud-native platforms, and distributed computing frameworks Strong knowledge of modern data modeling techniques: dimensional modeling, NoSQL, data virtualization Experience designing high-performance ETL/ELT and real-time data pipelines Deep understanding of data governance, security, metadata management, and access control Hands-on with CI/CD for data solutions, DataOps automation, and infrastructure-as-code (IaC) Proven cross-functional collaboration with executives, engineers, and analytics teams Strong problem-solving, strategic thinking, and technical leadership Experience with SQL/NoSQL databases, vector databases for LLMs Skilled in data modeling and performance tuning for OLAP and OLTP systems Experience with Apache Spark, Apache Airflow Familiarity with software engineering best practices: version control (Git/Subversion), CI/CD tools (Jenkins/Maven), automated testing, DevOps Good-to-Have Skills Experience with Data Mesh architectures and federated governance models Certifications in cloud data platforms or enterprise architecture Knowledge of AI/ML pipeline integration in enterprise data architectures Familiarity with BI & analytics platforms enabling self-service and enterprise reporting Education & Professional Certifications 9 to 12 years of experience in Computer Science, IT or related field AWS Certified Data Engineer (preferred) Databricks Certificate (preferred)