Fuel Cycle - Senior Data Engineer

• Proactive Ownership: You bring recommendations and solutions to your manager — you don’t wait to be told what to do. • Proactive Ownership: • Architectural Judgment: You have the judgment to make the right foundational decisions and defend them. • Architectural Judgment: • Greenfield Builder: You thrive on greenfield builds and take full ownership from design through to production. • Greenfield Builder: • Comfort with Ambiguity: You are comfortable with ambiguity and can translate high-level vision into a concrete engineering plan. • Comfort with Ambiguity: • Outsized Impact: You understand that on a small team your decisions have outsized and lasting impact. • Outsized Impact: • You have 5+ years of deep, hands-on experience building production lakehouses on Databricks. You write clean PySpark and Python, model data thoughtfully, and know how to build for a multi-tenant SaaS environment. • Deep production experience across the Databricks platform including Unity Catalog, Delta Live Tables, Databricks SQL, and Workflows • Delta Lake as a production table format — ACID transactions, schema evolution, performance optimization, and multi-tenant governance via Unity Catalog • Experience building and maintaining dbt transformation projects using the Databricks adapter in a production environment • PySpark for large-scale data transformation and batch pipeline authoring • Strong understanding of batch ingestion pipeline design — migrating from relational sources like MySQL and PostgreSQL into a lakehouse architecture • Experience with a modern pipeline orchestrator such as Dagster, Prefect, or Databricks Workflows; Dagster experience is a strong positive • Familiarity with vector databases, embedding pipelines, and RAG patterns for AI workloads — using tools such as Databricks Vector Search, pgvector, or Amazon OpenSearch • Exposure to AI agent and LLM-serving infrastructure including Amazon Bedrock, AgentCore, and Strands • Experience with data cataloging and governance tools such as Unity Catalog or OpenMetadata • Data modeling for multi-tenant analytical workloads — partitioning strategy, schema design, and tenant isolation patterns • Databricks on AWS — workspace configuration, S3 integration, IAM, and cost governance • Infrastructure as code using Databricks Asset Bundles or Terraform • Databricks certifications — Data Engineer Associate or Professional • Salesforce or CRM data integration experience • Prior experience in a multi-tenant SaaS environment with strict data isolation requirements • Experience migrating from OLTP to a lakehouse architecture • AWS-Native Experience — A Strong Positive: • Candidates with experience in AWS-native data services are strongly valued. Engineers who understand both Databricks and AWS-native approaches bring a broader architectural perspective that helps the team make better long-term platform decisions. • Apache Iceberg, AWS Glue, Athena, and DynamoDB experience