Lead Data Engineering: Real-Time ML Pipelines & Quality

Smadex

Austin (TX)

In loco

USD 140.000 - 190.000

Tempo pieno

14 giorni+
Generatore di candidature

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Vantaggi offerti da questo lavoro

Competitive compensation
Medical/dental/vision plans
Career growth opportunities
Travel to Barcelona HQ

Descrizione del lavoro

Smadex is seeking a Data Engineering Team Lead to oversee two sub-teams and architect scalable data pipelines for real-time ML inference. The role is based in Austin, TX or New York, NY and offers opportunities to drive data quality and governance at scale.

You will lead data quality and feature engineering initiatives, coordinate with infrastructure, and collaborate with executive leadership to ensure operational excellence and impactful data products.

Competenze

  • 6+ years in software or data engineering with 2+ years in leadership.
  • Advanced Spark and SQL skills and AWS experience.
  • Strong debugging and communication skills.
  • Master's degree or equivalent preferred.

Mansioni

  • Leading two sub-teams (Data Quality and Feature Engineering).
  • Designing scalable data pipelines for real-time ML inference.
  • Establishing data quality frameworks.
  • Enforcing data governance.
  • Overseeing feature store development.
  • Coordinating pipeline strategies with infrastructure.
  • Ensuring operational excellence.
  • Collaborating with executive leadership.

Conoscenze

Leadership
Spark
SQL
AWS
Debugging
Communication

Formazione

Master's degree

Strumenti

Docker
Kubernetes
Airflow
Git
Jira
Notion
Jenkins
MySQL
Redis

Descrizione del lavoro

Smadex is seeking a Data Engineering Team Lead to oversee two sub-teams and architect scalable data pipelines for real-time ML inference. The role is based in Austin, TX or New York, NY and offers opportunities to drive data quality and governance at scale.

You will lead data quality and feature engineering initiatives, coordinate with infrastructure, and collaborate with executive leadership to ensure operational excellence and impactful data products.

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