Senior Data Engineer

Fuel Cycle•Los Angeles, CA

5d•$185,000 - $200,000•Hybrid

About The Position

Fuel Cycle is seeking a Senior Data Engineer to join their new data engineering team, reporting directly to the VP of Engineering. This role is a founding member of the team, responsible for designing and building a Databricks-first data lake and pipeline infrastructure. This is a greenfield, foundational build that will define the future of the company's AI-native data foundation. The ideal candidate is experienced enough to make architectural decisions independently and drive the build without step-by-step direction, contributing meaningfully from day one.

Requirements

5+ years of deep, hands-on experience building production lakehouses on Databricks.
Clean PySpark and Python coding.
Thoughtful data modeling.
Experience building for a multi-tenant SaaS environment.
Deep production experience across the Databricks platform including Unity Catalog, Delta Live Tables, Databricks SQL, and Workflows.
Experience with 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.
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.
Strong Python and SQL skills.

Nice To Haves

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.
Experience in AWS-native data services (Apache Iceberg, AWS Glue, Athena, and DynamoDB).

Responsibilities

Design and build a Databricks-first data lake and pipeline infrastructure.
Create a multi-tenant data lake and warehouse that unifies all research data (surveys, qualitative feedback, CRM, media transcripts, etc.) in a structured, AI-consumable format.
Implement tenant-isolated data architecture where enterprise client data is structurally separated at the storage and query layer.
Develop provenance-aware data models where every data point carries full traceability back to its source.
Build batch ingestion pipelines to migrate and continuously sync data from existing relational databases and cloud storage into the new lake architecture.
Develop a nightly profile enrichment pipeline that rebuilds living user profiles from all data sources within each client account.
Create data access layers serving AI agents via MCP, qualitative search via RAG pipelines, statistical computation tools, REST APIs, and bulk export.