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.
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. Pattern finding and investigative analysis:
Detect patterns, anomalies, and recurring structures across datasets (e.g., differences in encodings, metadata conventions, module definitions, study and subject structures, derivations and outliers) 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.
Experience working in environments where data is consumed from multiple upstream sources and where documentation and metadata may be incomplete; ability to take initiative to close these gaps
and improve FAIR.
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.
Governance, compliance and ethics: 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.
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 (including learning clinical study conventions and data standards as needed).
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.
Postgraduate qualification in Biological Sciences is preferred.
Prior hands-on experience in the Pharma / Life Science Industry is required.
