Data Engineer

Data Engineer

Frisco, TX

Skill 1 Yrs of Exp Data Engineering
Skill 2 Yrs of Exp Data Bricks
Skill 3 Yrs of Exp Python

CORE RESPONSIBILITIES

• Execute against decomposed solution designs handed off by Senior Data Engineers, delivering production-grade code across the full vertical from ingestion through visualization.
• Land source data into the team's unified ingestion framework using the appropriate ingestion pattern (batch, CDC, streaming, API) as specified in the design.
• Implement physical data models and transformation logic in Snowflake (including Iceberg tables) and Databricks (Delta Lake, Unity Catalog) per the design.
• Apply medallion (bronze, silver, gold) architecture and the team's engineering standards to all data product builds, including naming conventions, documentation, and code review practices.
• Build assigned components of the semantic layer in Microsoft Fabric (Fabric IQ, OneLake) so business consumers interact with certified, business-meaningful models.
• Build assigned Power BI visualizations and reports following the design specifications and the team's BI standards.
• Implement comprehensive testing - unit tests, integration tests, data quality checks, reconciliation logic, and SLA-driven alerting.
• Own pipeline KTLO (Keep the Lights On) for assigned data products, including monitoring, incident response, and ongoing reliability improvements.
• Write and maintain documentation including source-to-target mappings, data lineage, data dictionaries, and runbooks.
• Contribute to and uphold the team's DevOps practices - Git, CI/CD, automated testing, and code review.
• Participate in HR business stakeholder meetings to build domain context, ask clarifying questions on assigned work, and grow into solution decomposition over time.
• Participate in design reviews led by Senior Data Engineers, contributing implementation perspective and growing toward leading designs independently.
• Collaborate with technical teams and share knowledge through demos and training sessions.
  

REQUIRED QUALIFICATIONS

Bachelor's degree in Computer Science, Software Engineering, Information Management, or equivalent experience in field - plus 4 years of related work experience.

• Must be located in the United States.
• 4 years of hands-on data engineering experience delivering production data pipelines in enterprise environments.
• Strong proficiency in SQL and Python, including PySpark and Spark SQL for distributed data transformation.
• Hands-on experience with Databricks including Delta Lake, Unity Catalog, and workflow orchestration.
• Hands-on experience with Snowflake at production scale.
• Working experience with Microsoft Fabric including OneLake; familiarity with Fabric IQ semantic layer concepts.
• Working experience building data visualizations and reports in Power BI.
• Experience implementing data ingestion pipelines using batch, CDC, API, or streaming patterns within a unified ingestion framework.
• Solid data modeling skills, including dimensional modeling and lakehouse modeling patterns at the physical implementation level.
• Experience implementing pipeline testing - unit tests, integration tests, data quality checks, and reconciliation.
• Experience with DevOps practices for data pipelines - Git, CI/CD, and automated testing.
• Good communication skills, with the ability to convey technical progress and ask clarifying questions of both technical leads and business stakeholders.
• Strong problem-solving skills and the ability to execute independently on well-defined technical work in a fast-paced, agile environment.

PREFERRED QUALIFICATIONS

• Experience with Iceberg tables or other modern open table formats.
• Experience with HR data domains - talent acquisition, workforce analytics, compensation, learning, performance, or people analytics.
• Familiarity with Workday, ServiceNow HR, or comparable HR systems of record as authoritative sources for analytics.
• Exposure to real-time streaming technologies including Kafka, Azure Event Hub, Delta Live Tables, or Spark Structured Streaming.
• Exposure to AI/ML pipelines or building data products that support ML workloads.
• Familiarity with legacy data platforms such as Teradata, Oracle, or SQL Server.
• Azure certifications or demonstrated experience with Azure-native data platform services.
• Familiarity with T-Mobile's Omni lakehouse platform, MagentaBuilt integrations, or enterprise IT architecture standards.
• Familiarity with data privacy and regulatory compliance for HR data (GDPR, CCPA, employee data protection).