Architect (ATC)
Cross study standardisation and harmonisation Support efforts to standardise 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 such as 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
FAIR enablement 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
Problem framing and stakeholder partnership 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
Data wrangling and analytical enablement Perform sourcing extraction joining transformation and reconciliation using Python SQL and AWS based tooling to support insights and downstream modelling
Required skills and experience
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 and standards quickly and apply them pragmatically including learning clinical study conventions and data standards as needed
Standardisation mindset Ability to propose consistent definitions and mappings across heterogeneous datasets balancing practicality traceability and reuse
Pattern recognition across datasets Comfortable comparing many studies and datasets to identify reusable logic common structures and exceptions that require explicit handling
Python and SQL Strong capability using Python and SQL for profiling reconciliation validation and repeatable analysis
AWS analytics foundations Experience working with AWS based data environments such as S3 and common query and processing services
Cross study standardisation and harmonisation Support efforts to standardise 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 such as 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
FAIR enablement 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
Problem framing and stakeholder partnership 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
Data wrangling and analytical enablement Perform sourcing extraction joining transformation and reconciliation using Python SQL and AWS based tooling to support insights and downstream modelling
Required skills and experience
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 and standards quickly and apply them pragmatically including learning clinical study conventions and data standards as needed
Standardisation mindset Ability to propose consistent definitions and mappings across heterogeneous datasets balancing practicality traceability and reuse
Pattern recognition across datasets Comfortable comparing many studies and datasets to identify reusable logic common structures and exceptions that require explicit handling
Python and SQL Strong capability using Python and SQL for profiling reconciliation validation and repeatable analysis
AWS analytics foundations Experience working with AWS based data environments such as S3 and common query and processing services