Responsibilities Include

- Design and maintain DBT models that produce trusted datasets, features, and metrics the Data Science team relies on for analysis, experimentation, ML, and reporting.
- Build and operate pipelines in Databricks - PySpark jobs and Delta/Iceberg tables - that turn raw operational events into analysis-ready data.
- Develop deep familiarity with operations so the datasets, schemas, and models you ship reflect how the business actually works.
- Orchestrate end-to-end data workflows in Airflow (and Prefect where it fits), with SLAs the DS team can count on for daily models, dashboards, and operational decisions.
- Participate in peer code reviews and raise the bar on data quality, testing, and documentation across the team's models.
- Partner with data scientists to scope, design, and productionize feature pipelines and the model-supporting data behind them.
- Optimize DBT and Spark workloads for cost, performance, and reliability as data volume grows.
- Learning new technologies quickly - nobody comes into this role knowing every piece of the stack. You'll lean on peers, documentation, and experimentation to grow.

What You BringBuild a strong business

- 5+ years of experience building, testing, and deploying data engineering systems.




- Experience with at least one distributed data system, and the ability to reason about consistency, latency, throughput, and fault tolerance.
- Strong SQL and proficiency with at least one of (py)Spark, DBT, or Airflow in production.
- Experience with Infrastructure-as-Code systems such as Terraform, AWS CDK, or

Pulumi.

- Understanding or strong interest in supply chain and the data challenges it creates.
- A self-starter who takes initiative, moves quick, and ships while collaborating on big challenges.
- Enthusiastic about working closely with team members to develop creative solutions to novel problems.
- Excellent written and oral communication in English.
- Tech: DBT, Databricks, (py)Spark, Airflow, Prefect, SQL, Iceberg/Delta; familiarity with the broader stack (Kinesis, EMR, Sigma, Pulumi) a plus.
- Comfortable using modern AI coding assistants (Claude Code, Cursor, Copilot, or similar) and experienced with AI-native workflows - prompting, agentic tooling,

evaluations, retrieval - and willing to bring them into your data engineering practice.

- A plus: Demonstrated, measurable success building with LLMs, evaluating model outputs, or integrating AI into data pipelines or internal tooling.

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