Cloud Data Engineer

RadNet•Rochester Hills, MI

About The Position

Artificial Intelligence; Advanced Technology; The very best in patient care. With decades of expertise, RadNet is Leading Radiology Forward. With dynamic cross-training and advancement opportunities in a team-focused environment, the core of RadNet’s success is its people with the commitment to a better healthcare experience. When you join RadNet as a Cloud Data Engineer, you will be joining a dedicated team of professionals who deliver quality, value, and access in the 21st century and align all stakeholders- patients, providers, payors, and regulators to achieve the best clinical outcomes.

Requirements

5+ years in data engineering with GCP experience.
Strong Python and SQL.
Hands-on with BigQuery, Dataflow, Pub/Sub, and Dataproc.
Experience with dbt/Airflow/Composer for orchestration.
Experience integrating APIs and SaaS sources using Airbyte.
Knowledge of data formats (Parquet, Avro, JSON, Delta Lake).

Nice To Haves

Experience with MongoDB Atlas or Neo4j AuraDB preferred.
Familiarity with vector search or graph analytics preferred.
Healthcare data experience (HL7, FHIR) preferred.

Responsibilities

Own end-to-end development of cloud ELT pipelines — from CDC/API ingestion (Airbyte) and batch loads (Cloud Build/Composer) into BigQuery landing tables to dbt transformations that publish curated Silver/Gold datasets.
Design and document Medallion-style models (Bronze → Silver → Gold) in dbt with clear naming, sources, exposures, and owners; maintain dbt project structure, packages, and environments.
Implement data quality with dbt tests (generic/custom), freshness checks, and documentation (dbt docs); publish artifacts to the Analytics Hub/catalog for discoverability.
Optimize BigQuery for dbt: partitioning, clustering, materializations (incremental/merge), cost controls (slots/quotas), job monitoring, and query performance tuning.
Support streaming use cases by landing real time data via Pub/Sub with BigQuery subscriptions (or Dataflow/Beam where required) and shaping near real time models with dbt incremental strategies.
Build orchestration and CI/CD for dbt using dbt Cloud or dbt Core (Cloud Build/Composer), with code review, automated tests, and artifact promotion across Dev/Test/Prod.
Partner with BI and AI/ML teams to expose trusted datasets and features; publish contract backed schemas and semantic conventions aligned to enterprise KPIs.
Migrate and reconcile legacy on prem pipelines (e.g., SQL Server CDC) into GCP; validate row level fidelity and handle late arriving and schema evolution scenarios.
Implement security, privacy, and governance (IAM, CMEK, BQ row/object level security, HIPAA); contribute to auditable data lineage (dbt exposures + warehouse lineage).
Establish monitoring/alerting for pipeline reliability (Cloud Monitoring/Logging), with SLAs/SLOs, retries/backfills, and incident runbooks; participate in on call as needed.