Note: The job is a remote job and is open to candidates in USA. Great Minds is a mission-driven organization that develops research-based, knowledge-rich curricula and educational tools for schools and teachers. The Senior Data Engineer will own reliable, scalable data pipelines and integrations, provide advanced platform support, and strengthen trusted analytics and reporting. The role also focuses on pipeline reliability, data quality, performance, security, deployment practices, and mentoring engineers.
Responsibilities
- Lead end-to-end delivery of complex pipelines and integrations — source onboarding, secure connectivity, ingestion configuration (primarily Fivetran), validation, deployment, and operational handoff — with documentation sufficient for handoff
- Provide advanced platform support across ingestion, transformation, orchestration, monitoring, and warehouse operations, owning outcomes rather than just tickets
- Design and maintain dbt (or similar) models that transform raw data into curated, tested, documented, consumption-ready datasets
- Establish engineering standards for pipeline development — naming conventions, load strategies, data contracts, error handling, testing — to speed up time-to-production for new integrations
- Own pipeline reliability: monitoring/alerting, SLAs/SLOs for freshness and success rate, incident response, and root-cause analysis that produces preventative fixes
- Improve platform performance and cost efficiency: warehouse sizing, job scheduling, query/pipeline tuning
- Implement secure-by-design practices: least-privilege access, secure sharing, privacy/compliance support
- Partner with data governance and analytics to improve data quality and trust via validation checks, reconciliation, freshness monitoring, and source-to-target documentation
- Support deployment and change management through version control, CI/CD, and environment promotions, reducing manual effort per release
- Mentor engineers through technical guidance and code review
- Translate business needs into well-scoped technical solutions and communicate tradeoffs clearly across technical and non-technical audiences
Skills
- 5+ years in data engineering, platform, or data operations, with demonstrated ownership of production pipelines and reliability — not just participation
- Advanced SQL; proven track record diagnosing and resolving production issues (ingestion failures, schema drift, anomalies, performance bottlenecks), with real examples
- Hands-on dbt (or comparable) experience building/maintaining data models in a cloud warehouse, including testing, documentation, and modular design
- Production experience with a modern cloud data warehouse; Snowflake strongly preferred (object management, performance, access patterns — not just SQL syntax)
- Production experience with managed ingestion tooling (Fivetran preferred), including connector troubleshooting and scaling
- Demonstrated experience implementing monitoring/alerting/operational processes that measurably improved reliability, MTTR, or freshness
- Strong communication across technical and non-technical stakeholders
- Strong prioritization skills; balances immediate fixes with long-term platform health
- Bachelor's degree in Computer Science, Engineering, Data Science, or a relative quantitative discipline
- Production experience with a modern cloud data warehouse; Snowflake strongly preferred (object management, performance, access patterns — not just SQL syntax)
- Production experience with managed ingestion tooling (Fivetran preferred), including connector troubleshooting and scaling
Benefits
- Remote work arrangement
Company Overview
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