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. Skills: Redshift, Data Engineer, dbt, Aws Experience: 6.00-12.00 Years