Azure ML Data Engineer – Medallion Platform

Randstad Digital Americas; Atlanta (GA); Sur place; USD 124 000 - 138 000; Plein temps

Résumé du poste

Randstad Digital Americas seeks a Senior Data Engineer to architect scalable data platforms, primarily on Azure, and enable ML models with governance and scalable data pipelines. You will lead data preparation, transformation, and delivery across the organization while aligning with Data Science teams.

Responsibilities include building ETL/ELT pipelines, adopting medallion architecture, and collaborating on model deployment.

Qualifications

• Bachelor's degree in Computer Science, Engineering, or related technical field.
• 7+ years of data engineering experience with lead or architect-level ownership.
• Strong Python and SQL with PySpark/pandas for large-scale transforms.
• Experience with medallion architecture and master data management.

Responsabilités

• Design, build, and optimize scalable ETL/ELT pipelines on Azure (Data Factory, Databricks, Functions, Synapse).
• Onboard external datasets into a medallion architecture aligned with master dimension tables.
• Migrate large datasets and refactor semi-structured data into analysis-ready structures.
• Establish enterprise-wide data governance, quality controls, and feature-ready models.
• Collaborate with Data Science to productionize ML models with code/docs standards.

Connaissances

Azure Data Factory

Databricks

PySpark

Python

SQL

Pandas

Master data

ETL

GitHub

ML models

Salesforce integration

Governance

Data pipelines

Dimensional modeling

Architecture

Data engineering

Formation

Bachelor's Degree

Outils

Azure Data Factory

Synapse

Databricks

GitHub

Python

SQL

PySpark

Salesforce APIs

Description du poste

Randstad Digital Americas seeks a Senior Data Engineer to architect scalable data platforms, primarily on Azure, and enable ML models with governance and scalable data pipelines. You will lead data preparation, transformation, and delivery across the organization while aligning with Data Science teams.

Responsibilities include building ETL/ELT pipelines, adopting medallion architecture, and collaborating on model deployment.