Data Engineer

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 fast, 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.