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Stellic is a fast-growing edtech startup transforming student success across higher education. The company partners with leading institutions like Cornell, Columbia, UVA, and Ohio State, serving over 1 million students across 7 countries. As a Lead Data Engineer, you will own the next phase of Stellic's data platform—a critical system powering internal decision-making and customer-facing insights for university partners.
You will be responsible for designing, building, and operating end-to-end data pipelines spanning ingestion, transformation, aggregation, and presentation. The role combines platform ownership with hands-on technical leadership. Key responsibilities include: architecting scalable, reliable data processing systems with strong correctness guarantees; designing data models that balance standardization with flexibility across diverse regional and institutional requirements; building and maintaining customer-facing data products that extend the core platform directly to partners; ensuring compliance with varied regional and partner-specific data requirements; and driving platform evolution to support new use cases and increasing scale.
The data team works with highly variable data across partners and regions, requiring thoughtful system design. You'll establish patterns for data correctness, consistency, and maintainability while collaborating closely with product, design, and customer-facing teams.
The current tech stack includes: Pydantic (schema), Terraform (infrastructure), dbt (transforms), MWAA/Airflow (orchestration), S3 + Redshift Serverless (storage), Glue Data Catalog + Schema Registry (catalog), Lake Formation ABAC (governance). Languages: Python, SQL. Cloud: AWS. Additional tools: PostgreSQL, ElasticSearch, Redis, GitHub, Sentry, Grafana, Prometheus, PostHog, Metabase.
You should have 8+ years of data engineering experience with previous technical lead experience. Deep expertise required in designing and operating data pipelines across ingestion, transformation, and aggregation; building scalable systems with correctness guarantees; designing flexible data models; understanding data lineage and validation; and ensuring compliance. Ideal candidates have experience building customer-facing data systems or data products. Strong communication skills and customer empathy are essential.
