Tech Lead / Senior Architect – Data Engineering with Lifesciences in London

Department: Life Sciences, Virtusa

Reports to: Client Partner, Life Sciences

Role Summary

Seeking a highly experienced Tech Lead / Senior Architect – Data Engineering with Lifesciences background to drive the design and implementation of enterprise-grade data platforms for a global pharma engagement. This role will focus on building scalable, secure, and compliant data solutions to support advanced analytics, clinical insights, and business intelligence.

Key Responsibilities

• Lead the end-to-end architecture, design, and implementation of scalable data platforms using Snowflake.
• Define and enforce data architecture standards, including modeling, naming conventions, and best practices.
• Design and implement robust ETL/ELT pipelines using DBT and cloud-native tools.
• Collaborate with global stakeholders (business, analytics, clinical, and IT teams) to translate requirements into scalable solutions.
• Drive data governance, lineage, cataloging, and quality frameworks across the platform.
• Ensure compliance with European data regulations (e.g., GDPR) and pharma-specific standards.
• Support efforts to standardize data across many studies with varying historical practices, evolving clinical data standards, and inconsistent conventions.
• Identify common structures and define consistent representations to enable cross-study analysis and reporting.
• Detect patterns, anomalies, and recurring structures across datasets and convert these findings into recommended mapping rules, validation checks, and documentation.
• Drive improvements toward FAIR (Findable, Accessible, Interoperable, Reusable) data by strengthening metadata, lineage, definitions, quality rules, and reuse guidance.
• Work with product owner, technical lead, data and business analyst leads to translate unstructured questions into clear data requirements, analytical approaches, pipeline outcomes, and acceptance criteria.
• Perform sourcing, extraction, joining, transformation, and reconciliation using Python, SQL, and AWS-based tooling to support insights and downstream modelling.
• Ensure compliant use of patient/study, clinical data aligned with GDPR, internal policies, and ethical standards; support access assessments and audit-ready documentation as needed.

Required Experience & Skills

• Analytical problem-solving in ambiguous contexts: Proven ability to solve complex problems where requirements are incomplete and the path forward requires investigation and iteration.
• Learning agility: Demonstrated ability and motivation to learn new domains, standards quickly and apply them pragmatically.
• Standardization mindset: Ability to propose consistent definitions and mappings across heterogeneous datasets, balancing practicality, traceability, and reuse.
• Python and SQL: Strong capability using Python and SQL for profiling, reconciliation, validation, and data engineering.
• AWS analytics foundations: Experience working with AWS-based data environments (e.g., S3 and common query/processing services).
• Clear communication and documentation: Ability to document “what the data means,” not only “what the code does,” in a way that supports reuse and governance.
• Clinical trial standards familiarity: Exposure to CDISC SDTM, ADaM (or similar concepts) and an interest in deepening this knowledge on the job.
• Clinical study domain: Familiarity with clinical study conduct and data flows, including privacy/consent principles and appropriate use of patient data.
• Regulated/pharma or life-sciences domain familiarity (oncology, diagnostics, regulatory terms like FDA PMA/510(k), IVDR) — speeds up prompt and content work enormously.
• Machine learning / AI / agentic tooling exposure: Familiarity with machine learning fundamentals, and/or experience supporting ML/AI use cases.
• Experience exploring LLM-based tooling, including agents is beneficial, especially when applied to documentation, metadata, and analytics workflows.

Preferred Qualifications

• Postgraduate qualification in Biological Sciences is preferred.
• Prior hands-on experience in the Pharma / Life Science Industry is required.