Data Platform Architect: Lakehouse & ML Delivery Lead

Zenfreed, LLC; Lansing (MI); Hybrid; USD 130.000 - 180.000; Vollzeit

Benefits dieser Stelle

Medical benefits

Dental benefits

Vision benefits

Hybrid work schedule

Zusammenfassung

Zenfreed, LLC in Lansing, MI seeks a Data Engineering Lead to design and deliver data pipelines, data lake/lakehouse architectures, and ML-enabled products within an agile DevOps framework. You will mentor engineers and collaborate with data scientists and product teams to ensure scalable, secure data solutions.

The role requires 4 years building data pipelines on cloud platforms (AWS/Azure/GCP) and 2 years enforcing data sharing policies, with hands-on skills in Python, SQL, Spark, Databricks,

Qualifikationen

• Bachelor's degree in CS/DS/IS.

Aufgaben

• Oversee the design, development, and maintenance of data pipelines that move, transform, and prepare data for analytics and operational systems.
• Architect and optimize data infrastructure, including storage solutions, orchestration frameworks, and ETL/ELT processes.
• Ensure data quality, reliability, and security by establishing standards, monitoring systems, and implementing validation and governance practices.
• Lead and mentor data engineering staff, providing technical guidance, reviewing designs/code, and supporting professional growth.
• Collaborate with cross functional partners, such as data scientists, analysts, product teams, and business stakeholders, to ensure data solutions meet organizational needs and strategic goals

Kenntnisse

Data modeling

Lakehouse

ML delivery

Agile DevOps

Distributed data

Ausbildung

Bachelor's degree in CS/DS/IS

Tools

Databricks

SageMaker

Spark

Kafka

Kubernetes

Power BI

Tableau

SQL

NoSQL/Graph DBs

GitHub

Jenkins

Terraform

DAGs

Docker

Jobbeschreibung

Zenfreed, LLC in Lansing, MI seeks a Data Engineering Lead to design and deliver data pipelines, data lake/lakehouse architectures, and ML-enabled products within an agile DevOps framework. You will mentor engineers and collaborate with data scientists and product teams to ensure scalable, secure data solutions.

The role requires 4 years building data pipelines on cloud platforms (AWS/Azure/GCP) and 2 years enforcing data sharing policies, with hands-on skills in Python, SQL, Spark, Databricks,