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# Principal Data Engineer

- **Company:** Xpertdox, LLC
- **Location:** Scottsdale, AZ, United States
- **Experience:** Expert
- **Salary:** $150,000 - $190,000
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Amazon S3, Apache HTTP Server, Code Review, Data Architecture, Information Engineering, Data Governance, Data Infrastructure, Extract Transform Load (ETL), Python (Programming Language), Standard Sql, Parquet, Data Layers, Data Lakes, Pyspark, AWS Glue, Data Management
- **Published:** October 22, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=d2e4a6380b7dd06d

## About the Role

* Led at least one production migration from legacy or file-based systems to a data lake or lakehouse architecture, carried through to completion.
 * Deep hands-on experience with AWS Glue, Athena, and S3.
 * 8+ years in data engineering or data platform roles, with demonstrated ownership of architecture decisions.
 * Experience making architecture decisions with incomplete information., * Strong working knowledge of open table formats, with Apache Iceberg preferred (Delta Lake or Apache Hudi also relevant), and columnar storage formats such as Parquet.
 * Strong Python and SQL skills; PySpark experience strongly preferred.
 * Experience designing for multi-tenant or multi-client data where schemas vary across sources.
 * Experience with data governance and security in a regulated environment.

## Description

You will own the move of our data from a batch-oriented, file-based environment to an AWS lakehouse, and you will own the data platform that results. The target architecture is Apache Iceberg tables on Amazon S3, queried through Amazon Athena, with AWS Glue handling ETL and cataloging. This is a hands-on role: you will design the architecture, build the critical components yourself, and set the standards the data team follows. Our analytics team's current pipelines remain the upstream source for the data you structure, and our self-serve analytics platform depends on the data layer you deliver. This role is not for someone who has designed a lakehouse but has never carried a production migration through to completion., In this role you will:

 * Own the design and execution of the lakehouse migration, from source ingestion through the query and reporting layer.
 * Design schema, partitioning, and table layout that hold up across many client datasets with varying structure.
 * Build and harden ETL pipelines in AWS Glue and PySpark, including upsert and merge logic, scheduling, failure handling, and monitoring.
 * Own data governance, access control, retention, and quality standards for protected health information (PHI).
 * Set and enforce engineering standards for the data platform: code review, testing, deployment, documentation, and cost management.
 * Own the data layer requirements for the self-serve analytics platform, so it supports natural-language querying and reporting.
 * Define and own the handoff between the analytics team's existing batch outputs and the lakehouse.
 * Provide technical leadership and mentorship to data engineering and analytics staff.