Job Description:
This is an individual-contributor role within the Content Strategy team in Norstella Content Operations, reporting to the Director, Analytics & Insights (Clinical RWD/RWE Solutions). It is a hands-on technical role that turns approved clinical specifications and business rules into robust, scalable, production-grade RWD pipelines and analytical outputs. Clinical qualification is not required; deep technical fluency in real-world data engineering and analytics is. The role is UK-based and remote.
Where the Director and the clinical specialists own what the evidence should be and why, this role owns how it is built -developing the technical business logic that operationalises clinical intent against real-world data, and ingesting, transforming and standardising large claims, EHR, lab and registry datasets so cohort and endpoint definitions are implemented correctly, at scale, and are performant, reproducible, well-documented and AI- and agent-ready. Focus areas:
● Data engineering - build and maintain the pipelines that turn raw RWD into analysis-ready, standardised, quality-controlled datasets
● Technical business logic - develop the technical business logic that translates clinical intent into buildable data logic, working with the clinical specialists to confirm the clinical side and surfacing edge cases the specification does not yet resolve
● Data quality & performance - own profiling, QC and optimisation so outputs are reliable and scalable
● AI- and agent-readiness (technical) - structure and expose data so it is retrievable, semantically consistent and reliably consumable by AI solutions and agentic workflows Responsibilities Own the technical build of RWD pipelines and datasets
● Develop the technical business logic that operationalises clinical intent - designing how cohort, endpoint and business-rule definitions are actually computed against messy real-world data, including join logic, temporal windows, episode and line-of-therapy rollups, deduplication and code-set operationalisation
● Partner with the clinical specialists to pressure-test that logic - taking their clinical definitions as the authority on what the evidence should mean, confirming the clinical side of ambiguous cases, and surfacing edge cases, data artefacts and implementation choices the specification does not yet resolve so they can be decided jointly
● Ingest, transform and standardise claims, EHR, lab and registry data into analysis-ready form, implementing the agreed cohort, endpoint and business-rule logic correctly and at scale
● Build performant, reproducible and well-documented data pipelines and analytical code in SQL and Python (and/or R or SAS), applying software-engineering discipline - version control, testing, code review and modular, reusable design




● Own technical and data-quality QC - data profiling, completeness and null checks, referential integrity, distributional and record-count validation, and reproducibility of pipeline outputs - surfacing anomalies and data-quality issues to the clinical specialists and Director for clinical interpretation, while retaining ownership of the technical-QC layer itself
● Optimise storage, compute and query performance across cloud data-warehouse environments as datasets and use cases scale
● Implement statistical validation and analytical routines to specification, and give substantive technical feedback on approach, feasibility and performanceEnable AI- and agent-ready outputs
● Structure and expose content and RWE outputs so they are retrievable, semantically consistent and reliably consumable by AI solutions, agentic workflows and downstream products
● Implement the technical guardrails, constraints and validation checks specified for agents interacting with RWD, and build and run the test harnesses that exercise agent outputs across representative and edge-case data - surfacing the results to the clinical Senior Specialist for the clinical release decision and re-running them on the agreed re-validation cadence
● Partner with Data Science, AI and Technology teams to productionise processes at scale and integrate RWD/RWE into AI-driven workflows Technical standards, tooling and reusability
● Build and maintain reusable technical assets - pipeline components, transformation libraries, standardised data models, QC frameworks and documentation, and the computational implementation of shared concepts such as line-of-therapy and endpoint logic (owning how each is computed, where the clinical Senior Specialist owns its clinical definition) - that let the team deliver faster and more consistently
● Establish and uphold engineering standards for documentation, versioning and governance of code and data logic, so implementations can evolve safely as data and requirements change
● Own the technical-feasibility lens on prospective RWD source assessment - evaluating structure, linkage, granularity, volume and integration effort - and advise the Director and Product on build complexity and constraints (the clinical-fit judgement sits with the clinical Senior Specialist; the Director owns the commercial go decision)




● Own and improve the processes and tooling the team uses to execute projects effectively
Collaboration and delivery
● Take clinical definitions and the evidence bar as owned by the Director and clinical specialists, and own the technical business logic that realises them - escalating where clinical intent is ambiguous, and flagging where a definition is technically infeasible or in tension with performance or scale
● Collaborate cross-functionally with Product, Data Science and Technology as a technical point of contact
● Deliver to a regular cadence against priorities set with the Director, and provide technical mentoring or oversight to more junior engineers or analysts
Qualifications
Essential
● 6+ years' hands-on experience building data pipelines and analytical solutions with large, complex healthcare or real-world datasets - such as open/closed claims, lab, EMR/EHR and registry data - including working with healthcare coding systems such as ICD-10 and NDC (clinical interpretation is provided by the clinical specialists; this role must handle the data structures fluently)
● Advanced SQL and Python (and/or R or SAS) - including performance tuning, complex transformations, and applying software-engineering discipline such as version control, testing and code review
● Hands-on experience with cloud data warehouses such as Amazon Redshift and Snowflake, including designing for scale, performance and cost
● Hands-on experience with statistical validation techniques and the ability to implement analytical and QC routines to a defined specification
● Ability to translate clinical definitions into technical business logic - designing how cohort, endpoint and business rules are computed against real-world data - and to work with clinical specialists to resolve edge cases, without requiring clinical qualification
● Excellent proficiency in Microsoft Office, including SharePoint, Excel and PowerPoint, Monday.com, LucidChart, Jira, Confluence, GitHub
● Impeccable or native English verbal and written communication, with experience working in global teams
Desirable
● Experience integrating RWD/RWE into AI solutions and agentic workflows, or structuring content to be AI- and agent-ready (retrieval, semantic consistency, vectorisation, evaluation harnesses)
● Familiarity with up-to-date data-engineering tooling and orchestration (e.g. dbt, Airflow, Spark) and CI/CD for data
● Working familiarity with the drug development lifecycle and how life-science data is captured and processed - enough to collaborate effectively with clinical and product colleagues
● Experience supporting the development of data, analytics, content or software products in life sciences or healthcare
● Prior experience mentoring junior engineers or analysts

📌 Data Analyst (Guwahati)
🏢 Straive
📍 Guwahati