3 дня назад
Data Engineer (AI)
Вакансия из Hirify Global, списка международных tech-компанийДля мэтча и отклика нужен Plus
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Data Engineer (AI) (Databricks/Python/SQL): Building platform infrastructure for product analytics, lakehouse data processing, and ML pipelines with an accent on data contracts, validation, lineage, and observability. Focus on designing self-service engineering workflows, enforcing PHI policies, evolving schemas safely, and operating reliable production data and ML systems.
Location: Remote within the United States
Company
is building an AI-powered health platform that combines lab testing, medical imaging, longitudinal data, and clinical and AI guidance.
What you will do
- Build tracking infrastructure for product analytics, experimentation, and feature gates with source-enforced event schemas.
- Develop Databricks Bronze, Silver, and Gold data processing infrastructure with schema evolution, contract tests, backfills, and monitoring.
- Build ML infrastructure for feature computation and serving, training and evaluation pipelines, and delivery of model output into the product.
- Create self-service templates, local development and preview environments, policy-as-code for PHI, ownership routing for alerts, and progressive deployment gates.
- Design declarative contracts, CI checks, validation gates, lineage, and rollback mechanisms that help engineers safely change data and ML systems.
- Operate production data or ML systems, participate in on-call support, and resolve failures under pressure.
Requirements
- Experience building internal platforms or infrastructure adopted by other engineers.
- Production experience operating data or ML systems, including on-call troubleshooting.
- Strong Python and SQL skills and experience with a lakehouse; Databricks is used, with Snowflake or BigQuery experience also relevant.
- Experience designing and evolving interfaces and schemas without breaking dependent teams.
- Experience with testing and CI for data or ML systems where correctness can fail silently.
- Approximately 1–4 years of engineering experience; demonstrated work matters more than the exact number of years.
Nice to have
- Experience with dbt, DLT, Dagster, Airflow, Kafka, or Spark Structured Streaming.
- Experience with data contracts, data diffing, lineage tooling, Terraform, feature stores, MLOps, or evaluation tooling.
- Experience with agentic coding workflows, healthcare, PHI, or HIPAA.
Culture & Benefits
- Flexible working hours and a collaborative, dynamic work environment.
- Competitive salary and benefits package.
- Emphasis on member-first design, clinical precision, transparency, and sustained integrity.
- Commitment to diversity, inclusion, and equal employment opportunity.
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