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# Data Engineer

- **Company:** Zifo Technologies Inc.
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Amazon S3, Batch Processing, Clinical Data Repository, Directed Acyclic Graph (Directed Graphs), Data Infrastructure, Data Integration, Data Integrity, Graph Database, Identity and Access Management, Intrusion Detection Systems, Python (Programming Language), PostgreSQL, Neo4j, Named Entity Recognition, Query Optimization, SPARQL, SQL Databases, AWS Cdk, Retrieval-Augmented Generation, Large Language Models, Database Optimization, Apache Spark, State Machines, Git, Fastapi, AngularJS, Pubmed, Data Lineage, Enterprise Integration, Front End Software Development, Asynchronous Programming, Functional Programming, Amazon Simple Queue Service (SQS), Terraform, Data Pipelines, Docker, Databricks
- **Published:** September 30, 2026
- **Apply:** https://www.builtincolorado.com/job/senior-data-engineer/11424094?handler=ApplyRedirect

## About the Role

* Strong Python development experience, including PDF/document parsing libraries such as PyMuPDF, pdfplumber, unstructured.io, or similar.
 * Advanced PostgreSQL-compatible SQL, including Amazon Aurora; experience with schema design, migrations, query optimization, and indexing strategies for large clinical datasets.
 * Hands-on experience with Neptune, Neo4j, or similar graph databases; proficiency in SPARQL or Cypher; experience with ontology and knowledge graph modeling for biomedical entities.
 * Experience with AWS services including Aurora PostgreSQL, S3, Lambda, Step Functions, SQS/SNS, and IAM, particularly for data pipeline orchestration.
 * Experience with PDF text extraction, document section classification, and named entity recognition (NER) for clinical/biomedical text; familiarity with embedding models and vector stores such as OpenSearch, pgvector, or Pinecone.
 * Experience building data-serving APIs using FastAPI, including asynchronous programming patterns and backend integration.
 * Experience preparing data for LangChain/LangGraph applications and designing RAG pipelines, including chunking, retrieval, reranking, and prompt-data integration.
 * Experience with Airflow, Prefect, AWS Step Functions, Temporal, or similar workflow orchestration tools; ability to design multi-stage DAGs with dependency management, retry logic, monitoring, and error handling.
 * Experience with Terraform or AWS CDK, Docker, and Git, including automated pipeline testing and deployment on AWS.

Domain Knowledge

 * Understanding of clinical trial structure: protocol sections (objectives, endpoints, eligibility criteria, study design, statistical considerations)
 * Familiarity with clinical data standards or terminologies (MeSH, MedDRA, SNOMED, ATC codes, CDISC) is a strong plus
 * Awareness of regulatory data integrity requirements (21 CFR Part 11, EU Annex 11, ALCOA+ principles)

Nice to Have

 * Experience with biomedical knowledge graphs (e.g., linking drugs -> targets -> diseases > trials)
 * Prior work with PubMed/MEDLINE data, ClinicalTrials.gov API, or EMA/CTIS data.
 * Apache Spark or Databricks for batch processing of large document corpora
 * dbt for transformation layer management over Aurora

## Description

* Design and implement data models in Amazon Aurora (relational) and GraphDB (knowledge graph) to represent trial design entities: endpoints, eligibility criteria, study arms, interventions, therapeutic areas, and their relationships
 * Develop embedding and vectorization pipelines to prepare extracted clinical text for RAG-based retrieval in LangGraph agentic workflows - chunking strategies, metadata enrichment, and vector store population
 * Build and maintain ETL/ELT workflows that transform unstructured clinical content into queryable, linked data across both relational and graph stores
 * Implement data quality validation specific to clinical data - protocol section classification accuracy, entity extraction completeness, cross-reference integrity (NCT IDs, EudraCT numbers, MeSH terms)
 * Build data serving APIs (Python/FastAPI) that expose curated datasets to the Angular frontend and LangGraph agent layer
 * Set up data lineage tracking and audit trails to support regulatory traceability of AI-generated trial design recommendation