Associate Engineer, Technology I

Responsibilities

• Lead and execute agile sprints with stakeholders from all business domains, gathering requirements and delivering actionable data solutions.
• Harmonize and integrate patient-level data (clinical trial, EHR/claims, etc.) across business lines.
• Partner closely with project owners to ensure data, tools, and AI solutions are scalable, fit-for-purpose, and impactful.
• Contribute to the organization’s long-term data/AI/tool strategies by sharing hands-on knowledge and workflow improvements.
• Drive engagement, adoption, and change management by actively collaborating with teams from early research.
• Design and build AI pipelines that ingest raw clinical trial data (Adverse Events, Lab Tests, Medical History, Procedures, Drug Names, Subject Exposure) and standardize it to FDA/SDTM regulatory formats using LLM-powered term resolution, phonetic matching, and symbolic rule engines.
• Analyze and validate clinical trial datasets across large study libraries to evaluate and confirm their transformation into CDISC SDTM data standards.
• Architect and maintain a Retrie-Augmented Generation (RAG) system that indexes OHDSI clinical documentation (THEMIS, CDM field specifications, dbt-synthea SQL patterns) into vector stores and injects relevant context into LLM inference for automated OMOP CDM field mapping.
• Build automated ETL SQL generation that reads source schemas via scan reports, produces Impala-compatible INSERT/SELECT statements, and populates OMOP CDM tables replacing months of manual mapping with a single pipeline command.
• Develop vocabulary resolution systems that map clinical codes (e.g., LOINC, MedDRA, ICD-10, RxNorm) to OMOP concept_ids using Athena vocabulary tables, embedding similarity, and LLM reasoning for ambiguous cases.
• Implement multi-layer data quality validation (DQD constraint checks, Achilles descriptive analysis, OmopCheckout sanity checks) translated from R/JDBC to Impala SQL, producing automated HTML quality reports after every ETL run.
• Build neuro-symbolic AI pipelines that route clinical term cleaning through four tiers — symbolic rules (YAML), phonetic algorithms, embedding similarity, and LLM inference — with full auditability and per-tier traceability on every decision.
• Design and develop full-stack applications (React frontend, Python-based API backend, Impala database layer) including a mapping review dashboard with confidence scores, RAG citations, and approve/reject workflows for stakeholder sign-off.
• Build and integrate interactive dashboards (Qlik Sense, Power BI) into applications to surface data-quality, mapping, and stakeholder insights.
• Build semantic classification models that automatically determine the meaning, data type category, clinical domain, sensitivity level, and OMOP mapping target for every column in any source database.
• Develop LLM-powered data profiling capabilities that analyze source schemas, profile tables and columns, visualize relationships, and generate natural-language documentation.
• Apply unsupervised machine learning (embedding-based clustering, such as K-Means) to standardize medical terminology, including indication terms.
• Design feedback loops where data quality validation failures and human corrections automatically generate new rules, surface vocabulary gaps, and expand training data — ensuring the system improves with every use.
• Lead and mentor cross-functional teams and multi-university student cohorts in building clinical data tools — for example, terminology and lab-name mapping tools delivered against defined timelines — translating complex technical requirements into achievable sprint deliverables.
• Partner across AbbVie divisions to scale EXTRACT through enablement sessions, cross-functional collaborations.

Qualifications

• Bachelor’s Degree in Computer Science, Data Science, Statistics, Mathematics, or related quantitative field required.
• Programming proficiency in Python and SQL required.
• Hands-on experience building applications with large language model APIs (Claude, GPT, or equivalent) including prompt engineering and Retrieval-Augmented Generation (RAG) architectures.
• Familiarity with clinical data standards such as OMOP CDM, CDISC SDTM, LOINC, and MedDRA (RxNorm a plus), or demonstrated ability to learn clinical informatics domains rapidly.
• Experience developing data visualizations and dashboards (Power BI, Qlik Sense).
• Demonstrated ability to learn, understand, and master new technologies rapidly.
• Strong written and oral English communication skills with the ability to present technical work to non-technical stakeholders.
• Ability to lead cross-functional teams and mentor junior developers or student cohorts.
• Analytical reasoning abilities, intellectual curiosity, and creativity in problem solving.