Data Warehouse Engineer

Parsons Corporation

About The Position

We are seeking a Data Warehouse Engineer to design, develop, and maintain scalable data warehouse solutions that support analytics, reporting, and application development. This role will focus on building efficient data pipelines, implementing structured data models, and integrating data from various source systems into centralized, analytics-ready environments. The selected candidate will work closely with analytics developers, application developers, and project teams to ensure data platforms are reliable, performant, and aligned with business and project requirements. This role requires strong SQL skills, experience with cloud-based data platforms (preferably Microsoft Azure), and a solid understanding of modern data warehousing best practices.

Requirements

Bachelor’s degree in Computer Science, Information Systems, Engineering, Data Analytics, or related technical field (or equivalent work experience)
3–7+ years of experience in data engineering, data warehousing, or related field
Strong proficiency in SQL, including query optimization, indexing, and performance tuning
Experience designing and implementing data warehouse architectures and dimensional data models
Experience developing ETL/ELT pipelines and data integration workflows
Experience with SQL Server Integration Services (SSIS)
Experience working with relational databases such as SQL Server, Azure SQL, or similar platforms
Experience working with structured and semi-structured data formats, including Parquet files

Nice To Haves

Experience working within Microsoft Azure data ecosystem, including tools such as: Azure Data Factory Azure SQL Database Azure Synapse Analytics Microsoft Fabric Azure Data Lake Storage
Experience supporting Power BI semantic models and analytics environments
Experience working with data lake architectures and modern cloud data platforms
Experience with scripting languages such as Python for data processing and automation
Experience supporting Power Platform solutions, including Power BI and Power Pages
Experience working in consulting or project-based environments

Responsibilities

Design, develop, and maintain enterprise data warehouse solutions using modern data warehousing principles
Build and manage ETL/ELT pipelines to ingest and transform data from various structured and unstructured sources
Develop and maintain dimensional data models optimized for analytics and reporting
Write efficient, scalable SQL queries and optimize database performance
Integrate data from systems such as relational databases, cloud platforms, APIs, SharePoint, flat files, and enterprise applications
Support data preparation and modeling needs for analytics platforms such as Power BI
Implement data validation, monitoring, and quality control processes to ensure data integrity
Collaborate with analytics and application development teams to support reporting and application requirements
Support cloud-based data platform development, primarily within the Microsoft Azure ecosystem
Work with both traditional ETL tools and modern cloud-based data integration tools
Document data models, pipeline processes, and system architecture
Design and implement data warehouse schemas, including fact and dimension tables
Develop ETL/ELT pipelines using Azure Data Factory, SSIS, Microsoft Fabric, or similar tools
Build and maintain Azure SQL databases, data lakes, Synapse, or Fabric-based warehouse environments
Transform and integrate data from multiple enterprise systems into centralized warehouse environments
Work with structured and semi-structured data formats, including Parquet files, CSV, and JSON
Optimize data structures and queries to improve performance and scalability
Support Power BI development teams by providing clean, structured, and performant data models
Assist in the design and implementation of cloud-based data platform architecture
Support backend data integration for Power Platform solutions, including Power BI and Power Pages
Troubleshoot and resolve data pipeline, performance, and integration issues
Assist in establishing and maintaining data engineering and data warehousing best practices