Principal Data Engineer

Vuori, Inc•Carlsbad, CA

1d•$181,000 - $242,000•Remote

About The Position

Vuori is seeking a Principal Data Engineer to design, build, and scale data pipelines that bring data into its platform and prepare it for analytics. The role spans the full pipeline lifecycle, from ingestion of heterogeneous raw sources through staging and curated layers, and helps set technical direction for the broader data infrastructure. The Principal Data Engineer partners with data engineers, data modelers, analysts, and product teams to determine priorities and raise technical standards across the team.

Requirements

Hands-on experience with Azure Data Factory, including pipelines, data flows, linked services, integration runtimes, and triggers.
Experience extracting and normalizing raw data from heterogeneous sources, including REST APIs, flat files, JSON/XML, and SFTP/blob-based drops.
Ability to write extraction logic that handles pagination, API-key or OAuth authentication, and incremental synchronization patterns.
Experience parsing and modeling semi-structured or nested JSON data in a cloud data warehouse, such as Snowflake VARIANT/FLATTEN.
At least 8 years of experience in data engineering or a related field, including experience operating at a senior or technical lead level.
Strong proficiency in SQL and at least one data engineering programming language: Python, Scala, or Java.
Strong communication skills and experience working cross-functionally with analytics, product, and engineering teams.

Nice To Haves

Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
Experience with dbt or similar transformation frameworks.
Familiarity with infrastructure-as-code tools such as Terraform or ARM/Bicep, and broader CI/CD practices.
Exposure to real-time analytics or streaming technologies.

Responsibilities

Move data through raw/source, staging, and curated layers, applying appropriate transformation, validation, and testing to prepare it for analytics consumption.
Build and maintain Azure Data Factory pipelines to orchestrate ingestion and transformation across source, curated, and serving layers.
Design ingestion patterns for REST/SOAP APIs, CSV and fixed-width flat files, JSON, and XML, reliably landing data in the source layer before transformation.
Build resilient API extraction logic that handles pagination, rate limiting, incremental or delta pulls, and schema drift.
Parse and flatten semi-structured JSON payloads, including nested objects and arrays, into queryable relational structures in Snowflake.
Manage file-based ingestion at scale, including SFTP/blob drops, file validation, schema enforcement, and reprocessing or backfill logic for late or malformed files.
Contribute to pipeline CI/CD using Azure DevOps or GitHub Actions across development, UAT, and production environments.
Set technical standards for pipeline design, code quality, and testing, and mentor other data engineers.