Senior Data Engineer — Pipelines, Lakes & Analytics

PRICE WATERHOUSE COOPERS; Detroit (MI); On-site; USD 95,000 - 125,000; Full time

Benefits offered by this job

Annual discretionary bonus

Medical, dental, and vision benefits

401k

Holiday pay

Vacation

Personal and family sick leave

Job summary

PwC is seeking a Senior Associate Data Engineer to design and build data infrastructure, pipelines, integration, and transformation solutions enabling efficient data processing and actionable client insights. The role leverages AWS, Azure, and Databricks to deliver scalable data architectures.

Responsibilities include developing data pipelines, optimizing data storage with data lakes and warehouses, and ensuring data quality and security across engagements.

Qualifications

• Bachelor's degree is required.
• Minimum 2 years of experience required.

Responsibilities

• Design and build data infrastructure and pipelines for clients.
• Develop and maintain data integration and transformation solutions.
• Leverage AWS and Azure to enhance data engineering capabilities.
• Apply data architecture and database management to optimize solutions.
• Utilize Airflow and Hadoop for scalable data processing and workflow management.
• Build and manage data lakes and data warehouses for large-scale storage.
• Ensure data quality through testing and performance tuning.
• Collaborate with clients to deliver actionable insights.
• Use Databricks for advanced analytics and visualization.
• Implement data security best practices and maintain compliance.
• Apply dimensional modeling and DAGs for efficient data organization.

Education

Bachelor's degree

Tools

AWS

Azure Data Factory

Databricks

Airflow

Hadoop

Job description

PwC is seeking a Senior Associate Data Engineer to design and build data infrastructure, pipelines, integration, and transformation solutions enabling efficient data processing and actionable client insights. The role leverages AWS, Azure, and Databricks to deliver scalable data architectures.

Responsibilities include developing data pipelines, optimizing data storage with data lakes and warehouses, and ensuring data quality and security across engagements.