Data Engineer – Azure Databricks

Highbrow LLC; Bellevue (WA); On-site; USD 135,000 - 180,000; Full time

Job summary

Data Engineer – Azure Databricks (Client: T-Mobile) is located in Bellevue, WA and requires on-site work. You will design and implement enterprise-scale data pipelines using Azure, Databricks, Spark, and Delta Lake to enable analytics and reporting.

Collaborate with data scientists, analysts, and engineers to build scalable data products, improve data quality, and optimize performance and costs. Strong SQL and cloud data warehousing skills are essential for success.

Qualifications

• Hands-on experience with Azure data platforms and services.
• Strong practical skills in designing and developing data pipelines.
• Experience with enterprise data governance, quality and security practices.

Responsibilities

• Partner with stakeholders to translate data needs into technical solutions.
• Design, build, test, and maintain scalable data pipelines using Azure, Databricks, Spark and Delta Lake.
• Develop architectures that support analytics, reporting, and operational use cases.
• Optimize transformations, ingestion, and data-processing workflows.
• Support Power BI solutions and collaborate with analysts and data scientists.
• Communicate designs, risks, and tradeoffs clearly to technical and non-technical audiences.

Skills

Azure Databricks

Apache Spark

SQL

Delta Lake

Power BI

Job description

Data Engineer – Azure Databricks (Client: T-Mobile) [Location: Bellevue, WA (On-site)]

Location: Bellevue, WA (On-site)

Job Description

• Strong hands-on experience designing and developing enterprise-scale data engineering solutions.
• Hands-on experience with Databricks and Apache Spark for large-scale data processing.
• Strong experience with Microsoft Azure data platforms and services.
• Hands-on experience designing and working with Delta Lake architectures.
• Hands-on experience with Microsoft Fabric and/or modern Microsoft analytics environments.
• Strong SQL skills.
• Hands-on experience designing and supporting ETL/ELT pipelines across multiple enterprise data sources.
• Experience working with structured, semi-structured, and distributed datasets.
• Understanding of modern data architecture, data modeling, data quality, and data integration patterns.
• Experience supporting analytical and reporting solutions, including Power BI.
• Ability to evaluate technical alternatives and make recommendations based on business requirements, scalability, maintainability, performance, and cost.
• Demonstrated ability to translate business objectives into technical requirements and implementation plans.
• Strong interpersonal skills and demonstrated ability to work effectively as part of a collaborative team.

Preferred Skills

• Experience supporting enterprise data environments spanning multiple business functions.
• Experience with complex cloud-based data ecosystems.
• Knowledge of data governance, security, lineage, and data quality practices.
• Experience optimizing Spark and Databricks workloads.
• Experience developing data products or reusable enterprise data assets.
• Familiarity with DevOps, CI/CD, and deployment automation.
• Strong people skills, team orientation, and professional judgment.
• Demonstrated ability to build productive working relationships and collaborate across teams.
• Excellent written and verbal communication skills.
• Ability to communicate effectively with senior leaders as well as deeply technical teams.
• Strong analytical and consultative problem-solving skills.

Job Responsibilities

• Partner directly with business stakeholders and leadership to understand strategic, analytical, and operational data needs.
• Translate business goals and high-level requirements into detailed technical requirements, data solutions, and implementation approaches.
• Design, build, test, and maintain scalable data pipelines using Azure, Databricks, Spark, and Delta Lake.
• Develop data architectures that support enterprise analytics, reporting, and operational use cases.
• Design and optimize data transformations, ingestion processes, and data-processing workflows.
• Support Microsoft Fabric and Power BI solutions.
• Analyze existing pipelines and architectures to identify opportunities to improve reliability, performance, scalability, and cost.
• Troubleshoot complex data integration, transformation, and performance issues.
• Work collaboratively with data scientists, analysts, architects, application teams, engineers, and business partners.
• Share knowledge, communicate openly, and contribute positively to team problem-solving and decision-making.
• Recommend technical approaches rather than relying solely on prescriptive requirements from stakeholders.
• Communicate technical designs, risks, tradeoffs, and recommendations clearly to both technical and non-technical audiences.
• Contribute to engineering standards, reusable patterns, documentation, monitoring, and data governance practices.