Senior Data Engineer, Analytics

Great Minds is a high-growth, mission-driven organization founded by educators in 2007. They are seeking a hands-on Senior Data Engineer to lead the delivery of reliable, scalable data pipelines and strengthen data platform support that enables trusted analytics and reporting across the organization.

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

Requirements:

• 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
• 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
• Bachelor's degree in Computer Science, Engineering, Data Science, or a relative quantitative discipline
• 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