Senior Architect

Position Summary

We are looking for an experienced Senior Data Architect to own data product architecture end to end, working closely together with Trinity domain experts to design and build data products that provide biopharma leaders the accurate, integrated evidence they need to inform high-stakes commercial decisions.

This is a hands-on role.

We are looking for an experienced architect who wants to both design and build.

Much of our raw material is unstructured or loosely structured text data; the core responsibility of the data architect in this role is to lead the teams that convert this unstructured data to enterprise-quality data products.

What You'll Do



* Work closely with domain experts, data scientists, and AI engineers to translate business requirements into data product architecture.


* Own the architecture for Trinity data product(s): tooling/stack, schemas, attribute inclusion/exclusion, grain and key design, entity resolution, data provenance, schema evolution strategy, etc.


* Design and build robust normalization pipelines for heterogeneous and loosely-structured text-based sources


* Build multi-stage pipelines that combine deterministic parsing, LLM-assisted extraction and inference, and human review and adjudication


* If desired, there is significant opportunity to take on AI-assisted full stack development: for example, to design and build internal interfaces analysts use to review, approve, and modify pipeline outputs (optional, if of interest)

What You'll Bring



* 8+ years of hands-on data engineering and data architecture experience, including owning architecture and technical direction for complex data systems end-to-end, from scoping and design through delivery and production support


* Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or a related technical field.


* Equivalent practical experience will be considered for strong candidates


* Depth in data modeling, with practical judgment on grain, keys, entity resolution, data/attribute provenance, and schema evolution.


* Strong SQL and relational and data warehouse expertise, including query and performance optimization


* Strong Python for production data pipelines, with orchestration, versioning, testing, and observability practices you have applied at scale


* Demonstrated experience normalizing messy semi-structured and unstructured sources – XML, PDFs, etc.

– into stable, documented schemas (core requirement)


* Ability to achieve significant productivity increases without sacrificing quality while leveraging AI coding assistants as part of the day-to-day engineering process


* Excellent written and verbal communication skills; ability to present technical concepts clearly to non-technical stakeholders and engage directly with business teams

Additional Skills



* Familiarity with PySpark and distributed data processing frameworks.


* Hands-on experience ...