Senior Cloud Data Engineer: Pipelines, Lakes & Analytics

Boston Medical Center; United States; Remote; USD 90,000 - 130,000; Full time

Benefits offered by this job

Medical, dental, vision

403(b) retirement matching

Paid time off

Bonuses and merit increases

Job summary

Boston Medical Center seeks a Senior Cloud Data Engineer to design and manage cloud-based data solutions, moving data between on-premises and cloud environments and ensuring security and compliance.

The role delivers data pipelines, data lakes/warehouses, ETL processes, and optimized queries. Collaboration with cross-functional teams shapes data needs and reporting across departments.

Qualifications

• Bachelor’s degree in computer science, information technology, or a related field.
• Minimum of five (5) years of experience in data engineering or related roles.
• Master’s degree preferred.

Responsibilities

• Builds data pipelines and scalable data processing systems in cloud environments.
• Designs, constructs, and tests data lakes, data warehouses, data lake houses, and databases.
• Designs and implements data pipelines using cloud-native tools and services.
• Develops ETL processes for data ingestion and transformation.
• Writes and optimizes SQL and Python queries for data retrieval and analysis.
• Collaborates with leadership, analysts, Research, and cross-functional teams to define data requirements and reporting needs.

Skills

Cloud platforms

Data warehousing

Data modeling

Process design

Communication skills

Microsoft Office

Python

SQL

Java

Education

Bachelor's degree in Computer Science / Information Technology

Master's degree in Computer Science / Information Technology

Epic Clarity and Caboodle Data Models certification

Microsoft Fabric or Amazon Redshift certification

Job description

Boston Medical Center seeks a Senior Cloud Data Engineer to design and manage cloud-based data solutions, moving data between on-premises and cloud environments and ensuring security and compliance.

The role delivers data pipelines, data lakes/warehouses, ETL processes, and optimized queries. Collaboration with cross-functional teams shapes data needs and reporting across departments.