Architect (Data Engineering)

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)