Tech Lead / Senior Architect – Data Engineering

Salary: £62,000 - 88,000 per year

Requirements:

• We require extensive experience as a Tech Lead or Senior Architect in data engineering, ideally within life sciences or pharma.
• We require strong hands-on experience with Python and SQL for profiling, reconciliation, validation, extraction, transformation, and data engineering.
• We require experience with AWS-based data environments and common cloud analytics tools.
• We require experience designing and implementing scalable data platforms, including Snowflake and ETL/ELT pipelines using DBT and cloud-native tools.
• We require proven analytical problem-solving skills in ambiguous contexts where requirements are incomplete and investigation is needed.
• We require the ability to define standards, mappings, and consistent representations across heterogeneous datasets.
• We require strong communication and documentation skills, including the ability to explain what the data means for reuse and governance.
• We require familiarity with clinical trial concepts and standards such as CDISC SDTM and ADaM, or similar.
• We require familiarity with clinical study conduct, data flows, privacy and consent principles, and appropriate use of patient data.
• We require understanding of regulated pharma or life sciences environments, including relevant compliance and regulatory terms.
• We require exposure to machine learning or AI use cases, and familiarity with LLM-based tooling is beneficial.
• We prefer a postgraduate qualification in Biological Sciences.
• We require prior hands-on experience in the Pharma or Life Science industry.

Responsibilities:

• We lead the end-to-end architecture, design, and implementation of scalable data platforms using Snowflake.
• We define and enforce data architecture standards, including modeling, naming conventions, and best practices.
• We design and implement robust ETL/ELT pipelines using DBT and cloud-native tools.
• We collaborate with global stakeholders across business, analytics, clinical, and IT teams to translate requirements into scalable solutions.
• We drive data governance, lineage, cataloging, and quality frameworks across the platform.
• We ensure compliance with European data regulations such as GDPR and pharma-specific standards.
• We support standardization of data across many studies with varying historical practices and inconsistent conventions.
• We identify common structures and define consistent representations to enable cross-study analysis and reporting.
• We perform pattern finding and investigative analysis across datasets, including anomalies, metadata conventions, module definitions, study and subject structures, derivations, and outliers.
• We convert analytical findings into mapping rules, validation checks, and documentation.
• We improve FAIR data capabilities by strengthening metadata, lineage, definitions, quality rules, and reuse guidance.
• We work with product owners, technical leads, and business and data analyst leads to turn unstructured questions into data requirements, analytical approaches, pipeline outcomes, and acceptance criteria.
• We perform sourcing, extraction, joining, transformation, and reconciliation using Python, SQL, and AWS-based tooling.
• We ensure compliant use of patient and clinical study data aligned with GDPR, internal policies, and ethical standards.
• We support access assessments and audit-ready documentation as needed.

Technologies:

• AI
• AWS
• Architect
• Business Intelligence
• Cloud
• ETL
• Support
• LLM
• Machine Learning
• Python
• SQL
• Snowflake
• dbt
• Product Owner

More:

We are Virtusas Life Sciences department, supporting a global pharma engagement focused on building enterprise-grade, scalable, secure, and compliant data solutions for advanced analytics, clinical insights, and business intelligence. This is a full-time individual contributor role with no travel required. We offer an experienced-level opportunity for someone who can work across complex, regulated data environments and help improve data standardization, governance, and FAIR principles across clinical and life sciences datasets.

last updated 34 week of 2026