Senior Data Engineer

AmeriSave Mortgage Corporation has set the standard in online mortgage lending with over $130 billion in funded loan volume. As one of the top-rated, largest privately-owned online mortgage lenders in the nation, our mission is to deliver beneficial, responsible home lending solutions with unwavering integrity, dedication and excellence. As a leading mortgage lender, we pride ourselves on our innovative approach and commitment to customer satisfaction.

Our employees are the driving force behind our success. We believe in the power of a dynamic and talented workforce and creating an environment where your contributions are not just recognized, they’re celebrated. Your success is our success, and we are seeking skilled professionals who are ready to bring their A-game, exceed benchmarks and enhance the overall excellence of AmeriSave, while also growing and advancing their careers.

At AmeriSave, we're one team with one shared dream - to be the best.   Let’s redefine excellence together!

What We’re Looking For:

We are seeking a highly skilled Senior Data Engineer to join the Enterprise Intelligence department at AmeriSave Mortgage. This role will be responsible for designing, developing, and maintaining a best-in-class enterprise data warehouse to support advanced analytics, data science, and Artificial Intelligence activities throughout the company. The Senior Data Engineer will report directly to the Senior Vice President of Enterprise Intelligence.

Observability & Monitoring

Own the control tower for database health across the Azure SQL estate — standing up and maintaining monitoring through database watcher, Azure Monitor metrics and alerts, Query Store, DMVs, and Log Analytics / KQL.

Establish wait-statistics, file-I/O, and resource (DTU/vCore) baselines per workload, defining what "normal" looks like per workload and per time-of-day.

Define and continuously tune Azure Monitor alert rules and action groups (blocking, deadlocks, long-running queries, resource saturation) against those baselines so alarms stay trusted and acted upon — alerting on statistical deviation rather than arbitrary thresholds.

What You’ll Do:

Design, develop, and maintain robust enterprise data warehouse solutions that support data science, artificial intelligence, and business intelligence requirements.

Architect scalable ETL/ELT pipelines to efficiently transform raw data into structured, analytics-ready formats.

Build and manage API integrations, including hands-on API development.

Utilize T-SQL and Azure Data Factory to create, optimize, and manage data integration workflows.

Use Microsoft Fabric notebooks for transformation and orchestration where appropriate, leveraging Lakehouse and Warehouse for supplemental storage.

Ensure high data quality, integrity, and performance through meticulous query tuning and process optimization.

Collaborate with data scientists, software developers, business intelligence teams, and stakeholders to develop and deploy data solutions that meet business needs.

Translate business requirements into technical solutions and coordinate smoothly between engineering and other teams.

Lead the creation of scalable, reliable data models and optimize them for performance and usability.

Drive continuous improvement in data engineering processes and practices to keep them efficient and aligned with industry best practices.

Monitor system performance and proactively implement improvements to maximize efficiency and scalability.

Troubleshoot and resolve data-related issues to ensure reliable data delivery.

What You’ll Need:

This role is ideal for someone who thrives in a dynamic, fast-paced environment, enjoys solving complex data problems, and is passionate about driving innovation in data engineering.

5+ years of hands-on experience in data warehousing, data engineering, or a similar role.

Extensive experience with T-SQL, including advanced query development and performance tuning.

5+ years as a SQL Server / Azure SQL DBA — performance tuning, index and statistics management, execution-plan analysis, and proactive capacity planning.

Proficiency with pipeline development and configuration using Azure Data Factory (ADF).

Working familiarity with Microsoft Fabric (notebooks and pipelines for transformation, Lakehouse, and Warehouse)

Expertise in Python for data engineering tasks, including data manipulation and workflow management.

Strong understanding of data modeling, data architecture, and best practices in data governance.

Experience handling sensitive/PII data and supporting data quality and governance in a regulated, financial-services environment.

Experience preparing clean, analytics- and ML-ready datasets to support data science and AI workloads.

Excellent problem-solving skills and the ability to work independently as well as collaboratively.

Strong communication skills to effectively liaise with both technical teams and non-technical stakeholders.

**Please note that the compensation information th…