Job Description

REQUIRED QUALIFICATIONS
1+ years of hands-on data engineering or software engineering experience, including internships and academic project work that demonstrate production-quality engineering habits.

• Working proficiency in SQL and Python.
• Exposure to Spark (PySpark or Spark SQL) for distributed data transformation, or strong willingness and demonstrated ability to learn it quickly.
• Exposure to cloud data platforms Databricks, Snowflake, or comparable with a willingness to develop hands-on depth.
• Familiarity with at least one BI tool (Power BI preferred, Tableau or similar acceptable). Understanding of fundamental data engineering concepts data modeling, ETL/ELT, data quality, source-to-target mapping.
• Familiarity with Git, code review practices, and basic CI/CD concepts. Good communication skills able to ask clarifying questions, communicate progress clearly, and learn from feedback.
• Strong problem-solving instincts, curiosity, and the discipline to deliver well-defined work to a high standard.
• Eagerness to learn the full vertical from ingestion through visualization, and to take on broader ownership over time.

PREFERRED QUALIFICATIONS

• Hands-on coursework, internship, or project experience with Databricks (Delta Lake) or Snowflake at any scale.
• Hands-on coursework, internship, or project experience with Microsoft Fabric, OneLake, or Power BI.
• Exposure to Iceberg tables or other modern open table formats. Exposure to HR data domains talent acquisition, workforce analytics, compensation, learning, performance, or people analytics.
• Familiarity with Workday, ServiceNow HR, or comparable HR systems of record.
  Exposure to streaming technologies (Kafka, Azure Event Hub, Delta Live Tables, or Spark Structured Streaming).
• Exposure to AI/ML pipelines or building data products that support ML workloads. Azure certifications (Azure Fundamentals, Data Engineer Associate) or working toward them.
  Familiarity with T-Mobile's Omni lakehouse platform, MagentaBuilt integrations, or enterprise IT architecture standards.
• Familiarity with data privacy concepts (GDPR, CCPA) and HR data handling considerations.

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