Leads the architecture, development, and production operation of complex data pipelines and platforms. Resolves scalability, data quality, reliability, and architectural challenges; evaluates data technologies; mentors engineers; establishes engineering standards; partners with product, analytics, and operations teams; communicates technical trade-offs; and conducts proofs of concept to guide platform strategy.
Everfi is a leading education technology company founded in 2008 that delivers digital learning solutions focused on real-world skills. The company provides scalable education in areas such as financial literacy, health and wellness, and workplace readiness.
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
Flexible 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
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