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# Principal Data Engineer

- **Company:** EVERFI, Inc.
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $160,000.0 - $170,000.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Airflow, Amazon Web Services, Architectural Patterns, Microsoft Azure, BigQuery, Code Review, Data Architecture, Information Engineering, Data Infrastructure, Data Warehousing, Python (Programming Language), Cloud Services, SQL Databases, Snowflake, Apache Spark, Kubernetes, Information Technology, Machine Learning Operations, Stream Processing, Databricks
- **Published:** July 4, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=c4791fda1d256184

## About the Role

* 7-10+ years of data engineering experience, with a track record of building complex, production-grade pipelines and platforms at scale
 * Deep expertise in data architecture, pipeline design, and platform engineering - including batch/streaming systems, data warehouse and lakehouse architectures, and tools like Airflow, Spark, dbt, or Databricks
 * Strong proficiency in cloud data platforms (AWS, GCP, or Azure) and services such as Snowflake, Redshift, or BigQuery. Snowpark and Snowflake ML/AI experience is a plus
 * Advanced skills in Python and SQL; proficiency in Scala or Java is a plus
 * A coaching mindset - you enjoy mentoring engineers and raising the bar for the team around you
 * Clear, confident communicator who can translate complex technical concepts for both technical and non-technical audiences
 * Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field; advanced degree preferred, * dbt Certified Developer
 * SnowPro Certification or advanced Snowflake certifications (Architect, Data Engineer, MLOps Engineer)
 * Experience with or certification in Apache Airflow

## Description

* Own the design, architecture, and implementation of complex data engineering initiatives on the team - including advanced pipeline development, data platform architecture, and solutions with significant downstream product or business impact
 * Identify and address difficult technical problems within the data platform, including scalability constraints, data quality issues, architectural debt, and reliability gaps, and develop solutions that are durable and aligned with organizational priorities
 * Evaluate and recommend data engineering technologies, tools, and architectural patterns - including cloud platform services, orchestration frameworks, and transformation tooling - with sound analytical judgment and awareness of long-term implications

Technical Mentorship and Standards

 * Serve as a technical mentor and senior resource for data engineers across the team, providing code review, architectural guidance, and hands-on coaching that accelerates the growth of engineers at earlier career stages
 * Contribute to the definition and documentation of data engineering standards, architectural patterns, and best practices that improve quality and consistency across the team's work
 * Participate in technical reviews - including architecture discussions, design reviews, and pull request feedback - contributing senior-level judgment that raises the quality of the team's collective output

Cross-Functional Technical Partnership

 * Partner with product, analytics, and operations stakeholders on complex data initiatives that require deep technical expertise - translating requirements into sound engineering solutions and surfacing trade-offs clearly
 * Represent data engineering in cross-functional planning conversations where data infrastructure decisions have product, analytical, or operational implications
 * Communicate complex data engineering concepts and architectural trade-offs clearly to both technical and non-technical stakeholders, enabling well-informed decisions across functions

Research and Technical Growth

 * Stay current with developments in data engineering, cloud platforms, and adjacent disciplines - evaluating emerging tools, frameworks, and architectural approaches for relevance to the team's direction
 * Conduct proof-of-concept work on promising approaches with clear evaluation criteria and well-framed recommendations
 * Share technical learning with the team in ways that are organized, actionable, and useful for planning and day-to-day engineering decisions


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