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# Senior Data Engineer

- **Company:** PrePass, LLC
- **Location:** Phoenix, AZ, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Clean Code Principles, Agile Methodology, Artificial Intelligence, Microsoft Azure, Code Coverage, Code Generation, Continuous Integration, Information Engineering, Extract Transform Load (ETL), Data Systems, Distributed Data Store, Apache Hive, Machine Learning, Pair Programming, Cloud Services, Software Engineering, SQL Server Integration Services, Systems Integration, Feature Store, Test-Driven Development (TDD), Feature Engineering, Sql Optimization, Large Language Models, Apache Spark, AI Coding Agents, Event Driven Architecture, Data Lakes, Pyspark, Core Data, Extreme Programming, Bicep, Machine Learning Operations, Terraform, Code Restructuring, Data Pipelines, Databricks
- **Published:** October 19, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=76eaa05154142fa8

## About the Role

* Senior-Level Data Engineering Experience: Proven track record of designing, building, and operating distributed data pipelines and cloud data platforms at scale.
 * Mastery of Test-Driven Development (TDD): Demonstrated hands-on expertise in TDD for data pipelines-writing unit, integration, and data-contract tests prior to implementation-alongside XP practices such as pairing, refactoring, and continuous integration.
 * AI Augmentation Proficiency: Practical experience integrating AI coding assistants and LLM-based tooling into daily engineering workflows to accelerate delivery, improve test coverage, and automate documentation.
 * Production Operations & Observability: Strong experience operating high-availability systems in production with clear uptime, reliability, and observability expectations.
 * Technical Leadership: Experience driving engineering standards, architectural reviews, documentation, and mentorship within an Agile team.

Bonus Points For

 * Experience in Data Science or Machine Learning, particularly in MLOps, model deployment, Feature Store management, or feature engineering on Databricks.
 * Experience with Infrastructure as Code (e.g., Terraform / Bicep) for managing Databricks workspaces and cloud resources.
 * Familiarity of event driven architecture - queues, batches, and pub/sub models.
 * Databricks Expertise:Production experience with the Databricks platform (Delta Lake, PySpark/Spark SQL, Databricks Workflows, and Unity Catalog)
 * Background in high-volume IoT, telematics, or transportation industry data.

## Description

PrePass is seeking a Senior Data Engineer to architect, build, and scale our core data platform. In this role, you will lead the design of high-throughput data pipelines and lakehouse architectures while mentoring fellow engineers in modern software craftsmanship. We are looking for a technical leader who treats data engineering with the rigor of software engineering-relying on strict Test-Driven Development (TDD), extreme programming (XP) practices, and AI-augmented workflows to deliver reliable, observable, and mission-critical data systems in Databricks., * Build & Scale Production ETL: Design, build, and operate scalable ETL/ELT pipelines on Azure that ingest, transform, and integrate data from multiple sources. Use advanced SQL, Apache Spark, and Lakehouse technologies such as Databricks and Delta Lake to support analytics, reporting, and real-time operational workloads.

 * Modernize Legacy Integrations: Partner with the team to define and execute a strategy for re-architecting legacy SQL Server Integration Services (SSIS) workloads into modern, cloud-native pipelines. Lead proof-of-concept and prototyping work and turn the findings into clear recommendations.
 * Champion TDD & XP Practices: Lead by example in writing clean, modular, and maintainable code using strict Test-Driven Development (TDD), pair programming, continuous refactoring, and automated CI/CD pipelines.
 * Drive AI-Augmented Engineering: Leverage AI-assisted development tools to accelerate code generation, automate unit and data-quality test creation, streamline legacy refactoring, and enhance pipeline anomaly detection.
 * Own Production Reliability & Data Quality: Operate and monitor mission-critical data systems against strict SLAs for data freshness, uptime, and reliability. Diagnose and resolve integration and data-quality failures, build end-to-end traceability and observability, and tune pipeline and compute performance for cost and scalability.
 * Elevate Engineering Standards: Define and evolve team-wide data engineering standards, architectural documentation, and continuous improvement initiatives within an Agile environment.