You have opted out of job matches in .
To undo this, go to the 'Skills and Interests' section of your preferences.
Shared by 
about 3 hours ago
warning
Heads-up
Requirements and responsibilities
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 BenefitsTarget 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 Plan100% Remote positionHealth, Dental, and Vision insurance401(K) with matching contributionGenerous Paid Time Off (PTO)Principal Data Engineer ResponsibilitiesDistinguished Technical ContributionOwn 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 impactIdentify 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 prioritiesEvaluate 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 implicationsTechnical Mentorship and StandardsServe 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 stagesContribute to the definition and documentation of data engineering standards, architectural patterns, and best practices that improve quality and consistency across the team's workParticipate 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 outputCross-Functional Technical PartnershipPartner 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 clearlyRepresent data engineering in cross-functional planning conversations where data infrastructure decisions have product, analytical, or operational implicationsCommunicate complex data engineering concepts and architectural trade-offs clearly to both technical and non-technical stakeholders, enabling well-informed decisions across functionsResearch and Technical GrowthStay current with developments in data engineering, cloud platforms, and adjacent disciplines — evaluating emerging tools, frameworks, and architectural approaches for relevance to the team's directionConduct proof-of-concept work on promising approaches with clear evaluation criteria and well-framed recommendationsShare technical learning with the team in ways that are organized, actionable, and useful for planning and day-to-day engineering decisionsPrincipal Data Engineer Qualifications7–10+ years of data engineering experience, with a track record of building complex, production-grade pipelines and platforms at scaleDeep 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 DatabricksStrong 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 plusAdvanced skills in Python and SQL; proficiency in Scala or Java is a plusA coaching mindset — you enjoy mentoring engineers and raising the bar for the team around youClear, confident communicator who can translate complex technical concepts for both technical and non-technical audiencesBachelor's degree in Computer Science, Engineering, Mathematics, or a related field; advanced degree preferredPreferred Qualificationsdbt Certified DeveloperSnowPro Certification or advanced Snowflake certifications (Architect, Data Engineer, MLOps Engineer)Experience with or certification in Apache Airflow
Optionally, you can add more information later (benefits, pre-screening questions, etc.)