Senior Healthcare Data Engineer

ASSYST•Austin, TX

6d•Hybrid

About The Position

ASSYST is seeking an experienced Senior Healthcare Data Engineer to lead system integration, programming automation, and data modernization initiatives for a client in Austin, Texas. The position focuses on evaluating requirements for electronic initial case report (eICR) ingestion, designing mechanisms to auto-populate registry fields, streamlining clinical review triage, and building operational dashboards. The engineer works closely with epidemiologists, clinical abstractors, and database architects to develop reliable automated workflows that extract, validate, and incorporate complex clinical and public health data into active surveillance workflows. The role includes assessing needs and defining system requirements for TBIRDS, translating epidemiological and clinical workflows into technical specifications and architecture recommendations, and producing System Requirements Documents and integration runbooks. It also involves building, testing, and maintaining data pipelines; automating record linkage and clinical review processes; monitoring performance through dashboards; establishing quality assurance frameworks; preserving clinical terminology semantics; troubleshooting and optimizing data ingestion; documenting solutions; and coordinating with technical, research, and healthcare reporting partners.

Requirements

At least 8 years of scripting and statistical programming experience, including hands-on development, testing, maintenance, and troubleshooting using SAS, Python, R, or comparable enterprise data languages.
At least 8 years of system integration and automation experience, including designing automated interoperability workflows, data exchange routines, and dashboard solutions.
At least 8 years of experience with health and database systems, including querying and managing complex structured or semi-structured healthcare, public health, or administrative datasets using relational databases and SQL.
At least 8 years of system requirements and technical analysis experience, including evaluating business and programmatic needs and translating them into formal technical specifications and system reports.
At least 8 years of data validation and quality assurance experience, including testing, auditing, and verification of automated data integration pipelines.
At least 8 years of technical documentation and communication experience, including explaining complex technical concepts to non-technical and clinical staff.

Nice To Haves

At least 2 years of healthcare interoperability and messaging experience, including eCR/eICR, HIE, EHR, and standards such as HL7 v2/v3, CDA, HL7 FHIR, XML, or JSON.
At least 2 years of experience with standardized clinical classifications such as SNOMED CT, LOINC, and ICD-10.
At least 2 years of public health and registry systems experience, including state public health registries, disease surveillance systems, data linkage or matching methods, and production-grade ETL pipelines.

Responsibilities

Conduct needs assessments to determine system and functional requirements for TBIRDS to ingest incoming eICRs and auto-populate designated registry fields.
Translate epidemiological and clinical surveillance workflows into technical specifications, data dictionaries, and architectural recommendations.
Produce formal System Requirements Documents (SRDs) and integration runbooks.
Design, build, test, and maintain automated data pipelines using SAS, Python, R, or SQL to extract, ingest, and process high-volume EHR data feeds.
Create automated rules for deterministic and probabilistic record linkage, entity deduplication, and integration across relational databases.
Automate clinical review workflows to triage and route incoming case alerts and reduce manual abstraction delays.
Build, configure, and maintain interactive operational dashboards tracking eCR ingestion volumes, data completeness, processing bottlenecks, and transformation error rates.
Present system and surveillance metrics to branch leadership, epidemiologists, and technical collaborators.
Establish unit, integration, and regression testing frameworks to validate pipeline reliability, data completeness, and standardization.
Ensure transformations preserve semantic meaning across clinical terminology sets and interoperability standards.
Debug scripts, address parsing anomalies, and optimize data ingestion throughput.
Maintain end-to-end documentation for scripts, schemas, data flows, and automated ETL procedures.
Coordinate between epidemiological researchers, IT infrastructure teams, and external healthcare reporting partners to align system modernization efforts.