Stellic is growing! We're looking for a Lead Data Engineer to own the next phase of our data platform that will power everything from internal decision-making to customer-facing insights - and we need a technical leader who can evolve it to support increasing scale, complexity, and product integration.

THE DATA TEAM

Stellic's data platform will be critical part of how we operate and deliver value, and will power everything from internal decision-making to customer-facing insights. The platform spans data ingestion, transformation, aggregation, and presentation - with much of the foundation in place, but requiring significant iteration and expansion to support new use cases and growing scale.

We work with data that is highly variable across partners and regions, requiring thoughtful system design to balance standardization with flexibility. In addition to the internal platform, this role is responsible for designing, building, and maintaining customer-facing data systems that extend the core architecture directly to university partners.

As a technical lead on Data, you will own the next phase of our data architecture, shaping both the platform and how data is leveraged across the product. This is a hands-on, high-impact role focused on building and operating a data platform that is scalable, adaptable, and aligned with both internal and product needs. This role will play a central part in shaping both the platform and how data is leveraged across the product.

ABOUT YOU

You've designed and operated data pipelines at scale before, and you understand what it means to build systems with clear guarantees around correctness and consistency. You think carefully about data models, lineage, and validation - and you're equally comfortable owning the internal platform and the customer-facing products built on top of it.

AS OUR LEAD DATA ENGINEER, YOU WILL OWN

• Data platform, end to end: Own the design, evolution, and operation of Stellic's data pipelines from ingestion through transformation, aggregation, and presentation.
• Customer-facing data systems: Design, build, and maintain customer-facing data products built on top of the core data architecture, extending the platform's value directly to university partners.
• Scalable, reliable data processing: Build and maintain systems that ensure consistent and reliable data processing at scale, with clear guarantees around correctness and behavior.
• Data models across varied requirements: Design data models and pipelines that balance standardization with flexibility to support diverse regional and institutional requirements.
• Compliance and partner requirements: Ensure systems meet regional and partner-specific data requirements and compliance constraints.
• Platform evolution: Identify and drive improvements across the data platform to support new use cases and increased scale. Establish patterns for data correctness, consistency, and maintainability.

TO BE SUCCESSFUL, YOU SHOULD HAVE

• 8+ years of data engineering experience with previous technical lead experience
• Deep experience designing and operating data pipelines across ingestion, transformation, and aggregation layers
• Strong expertise in building scalable data processing systems with clear guarantees around correctness and consistency
• Experience designing data models that balance standardization with flexibility across varied requirements
• Strong understanding of data correctness, lineage, and validation in complex data systems
• Familiarity with ensuring compliance with varied regional and partner-specific data requirements
• Ideal candidates will also have experience building and owning customer-facing data systems or data products
• Clear and effective communication skills for collaborating with product, design, and customer-facing teams
• Deep curiosity, customer empathy, and commitment to engineering as a craft

OUR CURRENT TECH STACK

While we plan on introducing new technologies and capabilities, and potentially migrating away from current ones, here is the general shape of our current stack:

• Data Pipeline:
  
  • Schema: Pydantic
  • Infrastructure: Terraform
  • Data transforms: dbt
  • Orchestration: MWAA / Airflow
  • Storage: S3 + Redshift Serverless
  • Catalog: Glue Data Catalog + Schema Registry
  • Governance: Lake Formation ABAC
• Languages: Python, SQL
• Frameworks: Django, Angular, React, Remix
• Databases: PostgreSQL, ElasticSearch, Redis
• Version control: Git (GitHub)
• Cloud: AWS
• Deployment: AWS CodeDeploy
• Observability & Analytics: Sentry, Grafana, Prometheus, PostHog, Metabase
• Both single and multi-tenant architecture