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Senior Data Engineer, AnalyticsGreat Minds (DC) • United States
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Senior Data Engineer, Analytics

Great Minds (DC)
  • US
    United States
  • US
    United States
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Über

Senior Data Engineer
Great Minds is a high-growth, mission-driven organization founded by educators in 2007. As a for-profit, Public Benefit Corporation, we believe all students deserve access to meaningful, challenging content—and all teachers deserve tools that are intuitive, effective, and built for the realities of today's classrooms. We develop high-quality, knowledge-rich math, science and ELA curricula grounded in research and designed in collaboration with educators. Our materials reflect real classroom needs and are built to drive lasting student outcomes. We are committed to usability, coherence, and practical implementation—supporting teachers not just through curriculum, but with professional learning, purposeful technology, and responsive service that enable strong adoption and impact. Our products—Eureka Math and Eureka Math², Wit & Wisdom®, Arts & Letters ELA™, PhD Science, and Geodes®—are trusted by thousands of schools and districts nationwide. These programs reflect a shared belief in high expectations, joyful rigor, and deep respect for educators and students. Great Minds is entering a new stage of growth and product maturity. We are focused on building more connected, customer-informed experiences across the full educator journey—from curriculum to professional learning to platform and support. Our long-term vision is to become a true partner in impact—not just delivering curriculum, but supporting educators in achieving outcomes at scale. Great Minds is seeking a hands-on Senior Data Engineer to lead delivery of reliable, scalable data pipelines and strengthen data platform support that enables trusted analytics and reporting across the organization. We are dedicated to generating and using data to inform strategy and evaluate performance—supporting our mission to drive change from leadership all the way to the classroom. This role is not primarily about building greenfield systems in isolation. It's about owning complexity in a production environment: inheriting integrations that may be imperfect, diagnosing failures under time pressure, and making deliberate decisions about when to fix versus when to rebuild. The right candidate has done this before and can describe not just what they built, but what broke, how they found it, and what they changed so it wouldn't break the same way again. You will own complex source integrations end-to-end, set engineering standards that measurably improve delivery speed and reliability, and build curated datasets using dbt (or similar) within our cloud data warehouse, including Snowflake. You will partner closely with analytics, data governance, and business stakeholders to deliver dependable data products, improve platform observability, and reduce operational burden through automation and best practices. Responsibilities Lead end-to-end delivery of complex pipelines and integrations—including new source onboarding, secure connectivity, ingestion configuration (primarily Fivetran), validation, production deployment, and operational handoff—with enough documentation that the next engineer doesn't need you in the room to keep things running. Provide advanced platform support across ingestion, transformation, orchestration, monitoring, and warehouse operations. "Support" here means owning outcomes, not just fielding tickets—you'll be accountable for dependable data delivery, performance, and efficient operations. Design and maintain dbt (or similar) models that transform raw data into curated, consumption-ready datasets. This includes testing, documentation, and modular design choices you can defend when an analyst asks why something was modeled the way it was. Establish and promote engineering standards for pipeline development—naming conventions, load strategies, data contracts, error handling, and testing—with the goal of reducing the time it takes a new integration to reach production reliably. Own pipeline reliability end-to-end: implement monitoring and alerting, define SLAs and SLOs around freshness and success rate, lead incident response, and drive root-cause analysis that results in preventative fixes rather than repeated patches. Improve platform performance and cost efficiency by analyzing workload and resource usage, identifying bottlenecks, and implementing concrete optimizations—including warehouse sizing strategies, job scheduling patterns, and query or pipeline performance tuning. Implement secure-by-design practices across the platform, including least-privilege access patterns, secure sharing approaches, and support for privacy and compliance requirements. Partner with data governance and analytics teams to enhance data quality and trust—implementing validation checks, reconciliation routines, freshness monitoring, and source-to-target documentation that make the data auditable, not just available. Support deployment and change management for pipelines and warehouse objects through version control, CI/CD patterns, and environment promotions, and actively work to reduce the manual effort required for each release cycle. Mentor other engineers through technical guidance, code reviews, and coaching on best practices—with the goal of raising the team's baseline capability, not just solving the immediate problem. Collaborate cross-functionally to translate business needs into well-scoped technical solutions, communicate tradeoffs clearly, and deliver high-impact enhancements to sources, pipelines, models, and platform capabilities. Job Requirements
Qualifications 5+ years in data engineering, platform or data operations, or related roles—with demonstrated, specific ownership of production pipelines and platform reliability, not just participation in building them. Advanced SQL proficiency and a track record of diagnosing and resolving production data issues including ingestion failures, schema drift, data anomalies, and performance bottlenecks. You can describe real examples of each. Hands-on experience with dbt or a comparable transformation framework to build and maintain data models within a cloud data warehouse, including testing, documentation, and the kinds of modular design patterns that hold up as the model layer grows. Experience supporting a modern cloud data warehouse in production. Snowflake experience is strongly preferred—specifically practical knowledge of object management, performance considerations, and access patterns, not just familiarity with SQL syntax in a Snowflake environment. Experience operating managed ingestion tooling such as Fivetran in a production environment, including troubleshooting connector failures and scaling ingestion workloads as source systems and volumes change. Demonstrated experience implementing monitoring, alerting, and operational processes that measurably improve reliability, reduce MTTR, and increase data freshness consistency—with outcomes you can speak to. Strong communication skills with both technical and non-technical partners. You can explain a data contract to an analyst and a pipeline failure to a VP without the same script. Strong time management and the ability to balance competing priorities without losing sight of longer-term platform health. Commitment to excellence and a high level of integrity. Required Education Bachelor's degree in Computer Science, Engineering, Data Science, or a relative quantitative discipline. Status Full-time Location Remote The expected base salary range for this position is $88,000-$97,000, however the offered salary may be higher or lower than the above range dependent on numerous factors including, but not limited to location, work experience, skills and internal equity considerations. The base salary is not inclusive of benefits or other incentives. A cover letter and resume are required to be considered for this position. New employees will be required to successfully complete a background check. Any communication to applicants relating to the Great Minds hiring process will only come from email addresses with the domains greatminds.org or greatminds.recruitee.com. If in the course of the application or hiring process with Great Minds you are contacted through another domain, are requested to provide banking or other sensitive information, or you note any other suspicious activity, please contact security@greatminds.org. Great Minds is an equal opportunity employer. We will extend equal opportunity to all individuals without regard to race, religion, color, sex (including pregnancy, sexual orientation, and gender identity), national origin, disability, age, genetic information, or any other status protected under applicable federal, state, or local laws. Our policy reflects and affirms the organization's commitment to the principles of fair employment and the elimination of all discriminatory practices. #LI-Remote
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