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Salary: USD 173,000 - 222,495 / annual
Vuori is seeking a Principal Data Engineer to design, build, and scale data pipelines that ingest, transform, and prepare data for analytics across the organization. You will own the full pipeline lifecycle—from raw ingestion of heterogeneous sources through staging and curated layers—while setting technical direction for the broader data infrastructure team.
Key responsibilities include:
- Designing and implementing end-to-end data pipelines across raw/src, staging, and curated layers with appropriate transformation, validation, and testing at each stage
- Building and maintaining Azure Data Factory (ADF) pipelines to orchestrate ingestion and transformation across all layers
- Designing ingestion patterns for diverse raw source formats including REST/SOAP APIs, flat files (CSV, fixed-width), JSON, and XML
- Developing resilient API extraction logic that handles pagination, rate limiting, incremental/delta pulls, and schema drift from third-party sources
- Parsing and flattening semi-structured JSON payloads (nested objects, arrays) into queryable relational structures within Snowflake
- Managing file-based ingestion at scale—SFTP/blob drops, file validation, schema enforcement, and reprocessing/backfill logic for late or malformed files
- Contributing to ADF pipeline CI/CD practices, deploying through Azure DevOps/GitHub Actions across dev/UAT/prod environments
- Setting technical standards for pipeline design, code quality, and testing; mentoring other data engineers on the team
You will partner closely with fellow data engineers, data modelers, analysts, and product teams to determine priorities and help raise the technical bar across the organization.
Requirements:
- 8+ years of experience in data engineering or related field, including senior or technical lead level work
- Hands-on expertise with Azure Data Factory (ADF)—pipelines, data flows, linked services, integration runtimes, and triggers
- Proven experience extracting and normalizing raw data from heterogeneous sources: REST APIs, flat files, JSON/XML, and SFTP/blob-based drops
- Comfortable writing extraction logic handling pagination, authentication (API keys, OAuth), and incremental sync patterns
- Experience parsing and modeling semi-structured/nested JSON data within a cloud data warehouse (e.g., Snowflake VARIANT/FLATTEN)
- Strong SQL proficiency and fluency in at least one programming language commonly used in data engineering (Python, Scala, or Java)
- Strong communication skills and cross-functional experience working with analytics, product, and engineering teams
- Bachelor's degree in Computer Science, Engineering, or related field, or equivalent practical experience preferred
- Preferred: experience with dbt or similar transformation frameworks; familiarity with infrastructure-as-code (Terraform, ARM/Bicep) and CI/CD practices; exposure to real-time analytics or streaming technologies
