Sr. Data Warehouse Engineer

Senior Data Warehouse Engineer

Location: Irving, TX (Onsite 5 Days/Week)

Employment Type: Contract-to-Hire (4–6 Months)

Work Authorization: Must be authorized to work in the U.S. without sponsorship

Position Overview

Our client is a Fortune 500 Insurance company seeking a highly technical Senior Data Warehouse Engineer to help modernize and scale a large enterprise data environment supporting critical operational and supply chain functions. This individual will play a key role in designing and implementing a long-term data strategy that reduces reliance on transactional systems while improving data availability, reporting performance, and scalability.

The ideal candidate will have deep expertise across Oracle, SQL Server/Azure SQL, Snowflake, and Azure Data Factory, with the ability to architect efficient data movement and transformation processes for large, complex datasets. This role requires someone who can move beyond simply building pipelines and instead understand source system architecture, optimize data structures, and design scalable warehouse solutions capable of supporting enterprise analytics and reporting.

This is a hands-on engineering position for someone who enjoys solving complex data challenges, optimizing performance, and building modern data platforms that support long-term business growth.

Key Technologies

• Azure Data Factory (ADF)
• Azure SQL Database / SQL Server
• Oracle PL/SQL
• Snowflake
• JSON Data Processing
• REST APIs
• Data Warehousing
• Data Modeling
• Power BI

Primary Responsibilities

• Design, develop, and maintain scalable data pipelines using Azure Data Factory.
• Build and optimize enterprise data warehouse solutions supporting operational reporting and analytics.
• Design and implement incremental data loading strategies utilizing UPSERT, MERGE, CDC, and other efficient synchronization techniques.
• Extract, transform, and load data from large-scale Oracle environments into Azure SQL and Snowflake platforms.
• Analyze complex source systems and identify optimal approaches for data extraction, transformation, and storage.
• Design and maintain dimensional data models, including star and snowflake schemas.
• Transform semi-structured data, including complex JSON payloads, into consumable relational datasets.
• Develop data integration solutions leveraging REST APIs, sFTP-based workflows, and other external data sources.
• Improve query performance through indexing strategies, partitioning, optimization, and database tuning.
• Establish data engineering standards, best practices, naming conventions, and architectural guidelines.
• Collaborate closely with business intelligence and reporting teams to ensure data accuracy, consistency, and performance.
• Troubleshoot and resolve issues across ingestion, transformation, storage, and reporting layers.
• Mentor team members and provide technical leadership on data engineering best practices.

Required Qualifications

• 8 years of experience in Data Engineering, Data Warehousing, or related disciplines.
• Advanced SQL expertise across Oracle, SQL Server/Azure SQL, and Snowflake.
• Strong hands-on experience developing and supporting Azure Data Factory solutions.
• Demonstrated experience working within large-scale enterprise data environments.
• Deep understanding of incremental data processing methodologies, including UPSERT and MERGE patterns.
• Strong Oracle PL/SQL development experience.
• Experience designing and optimizing large-scale relational databases.
• Proven ability to tune complex SQL queries and optimize database performance.
• Experience working with JSON and other semi-structured data formats.
• Experience integrating data from ERP systems, operational platforms, APIs, and external file-based sources.
• Strong understanding of dimensional modeling concepts and enterprise data warehouse design.
• Ability to evaluate source system structures and design efficient downstream data architectures.

Preferred Qualifications

• Experience supporting supply chain, logistics, manufacturing, or operational data environments.
• Experience working with large ERP platforms such as Oracle EBS or similar enterprise systems.
• Experience implementing enterprise data modernization initiatives.
• Exposure to Power BI and enterprise reporting solutions.
• Experience with data governance, data quality, and master data management initiatives.

What Success Looks Like

• Reduce reporting dependency on transactional production systems.
• Improve reporting reliability and performance through optimized warehouse architecture.
• Implement scalable incremental data ingestion and synchronization processes.
• Create sustainable, maintainable data structures that support long-term business growth.
• Establish engineering best practices that improve efficiency, consistency, and scalability across the data platform.