Technical Solutions Engineer - Pre / Data Refinery

Duration: 6+ month contract with possibility of extensions or conversion

Start Date: ASAP

Client Overview: Our client is a healthcare data and technology company focused on AI-enabled precision health solutions. the client transforms multimodal health data into insights, models, and actions designed to accelerate biomedical research and improve healthcare.

Job Description: The Technical Solutions Engineer will join our clients Data Refinery team, partnering with healthcare organizations, life sciences companies, research institutions, and data stewards to transform fragmented healthcare and research data into scalable, curated, analysis-ready datasets. This person will serve as a customer-facing technical advisor and hands-on implementation partner, helping customers ingest, harmonize, enrich, and operationalize complex healthcare and research data. Responsibilities include designing cloud-native data architectures, building and optimizing ingestion and transformation pipelines, integrating EHR and research data, supporting FHIR/OMOP standardization, improving data quality, and taking customer implementations from discovery and proof of concept through production deployment. The engineer will also partner with Product and Engineering to turn customer feedback into reusable technical assets, implementation playbooks, automation, and future product capabilities.

Skill Set Requirements: 6+ years relevant experience. Healthcare technology, data engineering, technical consulting, or solutions engineering background. Python. Golang. SQL.Terraform. YAML.GCP, AWS, and/or Azure. ETL/ELT and data pipeline development. Structured and unstructured data processing.verily Cloud-native solution architecture. Data ingestion, transformation, and curation. Strong customer-facing technical communication. Ability to translate business requirements into technical solutions. Strong project ownership and ability to manage multiple concurrent engagements.

Nice to Haves: Healthcare/life sciences experience. Epic, Cerner, Allscripts, or other EHR systems. FHIR.HL7.CCD/C-CDA.OMOP.ICD, SNOMED, and LOINC.BigQuery. Snowflake. Databricks. Spark. Patient identity resolution/matching. Clinical data harmonization. Longitudinal patient record generation. Data quality frameworks. NLP and unstructured clinical data. AI/ML workflows. Biomedical data abstraction. API development and healthcare interoperability. Education/Certification(s)/License(s):BA/BS in Computer Science, Engineering, Biomedical Informatics, Health Informatics, Data Science, or a related field, or equivalent practical experience. No specific certification or license required.