Principal Data Engineer at EVERFI

, , United States -

Full Time

Start Date

Immediate

Expiry Date

30 Sep, 26

Salary

170000.0

Posted On

02 Jul, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Architecture, Pipeline Design, Platform Engineering, Python, SQL, Airflow, Spark, Dbt, Databricks, Snowflake, AWS, GCP, Azure, Technical Mentorship, Scala, Java

Industry

E-Learning Providers

Description

We are looking for a Principal Data Engineer to join our Data Engineering team as a senior individual contributor and technical leader. This is a high-impact role for an engineer who thrives on owning complex and consequential data engineering challenges — from architecture through production — while also elevating the craft and capabilities of the engineers around them. Principal Data Engineer Compensation and Benefits Target base salary range: $160,000 to $170,000 depending on experience and education. Everfi may pay more or less based on employee qualifications, market value, Company finances, and other operational considerations. This role is eligible to participate in the Corporate Bonus Plan 100% Remote position Health, Dental, and Vision insurance 401(K) with matching contribution Generous Paid Time Off (PTO) Principal Data Engineer Responsibilities Distinguished Technical Contribution 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 Principal Data Engineer Qualifications 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 Preferred Qualifications dbt Certified Developer SnowPro Certification or advanced Snowflake certifications (Architect, Data Engineer, MLOps Engineer) Experience with or certification in Apache Airflow

Responsibilities

Lead the design, architecture, and implementation of complex data engineering initiatives and platforms. Provide technical mentorship and establish engineering standards to elevate the team's overall technical capabilities.