- 10+ years working with high-volume data infrastructure.
- Core stack: Databricks, AWS (EMR, Kinesis/Kafka, S3), Apache Spark/Spark Streaming, Apache Airflow (MWAA), SQL, Python (Java/Scala is a plus).
- AI-native mindset: You already use LLM-based dev tools daily, not as a novelty, but as a force multiplier. You can evaluate when AI-generated code is correct, refactor prompts like you refactor code, and build agentic workflows that compound your team's output.
- Experience with data quality frameworks (Great Expectations, DQX, or similar): validation rules, schema enforcement, automated monitoring.
- Proven ability to architect logical/physical data models, optimize SQL, and tune system performance.
- Familiarity with IaC tools (Terraform) for cloud infrastructure provisioning.
- Strong communicator who works independently and ships with minimal supervision.
- BS in CS, Engineering, Math, or equivalent hands-on experience.
AI-Native Expectations
Daily use. Our engineers use Claude Code every day to scaffold pipelines, write and review code, generate tests, and draft documentation. Our codebase includes custom Claude skills that enforce team conventions, and we use Databricks Genie to answer common data questions for non-technical users.
Judgment and ownership. You own everything you ship, whether you or a model wrote it. You review AI-generated code as if you wrote it, test it as if it were going to production, and refactor it when it doesn't meet our standards.
Velocity. We expect AI fluency to show up as output: faster pipeline delivery, broader test coverage, and better documentation, without lowering quality.
Team leadership. You share what works, contribute to our shared AI tooling (Claude skills, agent workflows, prompts), and help other engineers get more out of their AI tools.
Continuous learning. AI tooling changes quickly. You stay current on new tools and techniques, try them on real work, and bring recommendations to the team.