Senior Data Engineer

We are building the next generation of our platform — an AI-native data foundation that will power intelligent agents, living user profiles, digital twins, and conversational research analytics. This is a greenfield, foundational build that will define the future of the company.

We are building a new data engineering team reporting directly to the VP of Engineering. You will be a founding member of this team, responsible for designing and building the Databricks-first data lake and pipeline infrastructure that every future AI product will depend on.

This is not a maintenance role. We need engineers who are productive from day one — experienced enough to make architectural decisions independently and drive the build without step-by-step direction.

• A multi-tenant data lake and warehouse that unifies all research data — surveys, qualitative feedback, CRM, media transcripts, and more — in a structured, AI-consumable format

• Tenant-isolated data architecture where enterprise client data is structurally separated at the storage and query layer

• Provenance-aware data models where every data point carries full traceability back to its source

• Batch ingestion pipelines that migrate and continuously sync data from existing relational databases and cloud storage into the new lake architecture

• A nightly profile enrichment pipeline that rebuilds living user profiles from all data sources within each client account

• Data access layers serving AI agents via MCP, qualitative search via RAG pipelines, statistical computation tools, REST APIs, and bulk export

• Quickly integrates with the engineering team and contributes meaningfully to the data platform build

• Takes ownership of assigned pipeline and infrastructure work end-to-end, from design through production

• Brings architectural recommendations and solutions proactively, rather than waiting for direction

• Demonstrates strong collaboration and communication across engineering and product teams

• VP of Engineering — your direct manager and the data engineering team’s founding sponsor

• Peer Senior Data Engineers on the founding data engineering team

• AI product and platform engineers consuming the data foundation you build

• Product and engineering stakeholders across the Fuel Cycle platform

• 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

• Strong Python and SQL skills

• 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

• 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

At Fuel Cycle, we embrace the values of diversity, equity, and inclusion and are committed to fostering an inclusive company culture. We believe that everyone, regardless of their background or identity, should have equal access to opportunities for growth and advancement. Our selection processes and career pathways are designed to be fair, transparent, and free from bias. We value the unique perspectives and contributions of each team member, knowing that this diverse range of experiences strengthens our team. Fuel Cycle stands firmly against discrimination based on disability, sex, race, gender identity, sexual orientation, religion, national origin, age, veteran status, or any other protected status under the law.

Fuel Cycle is an equal opportunity employer and fully comply with the Americans with Disabilities Act (ADA). We will provide reasonable accommodations for qualified applicants and employees with disabilities, as needed, to enable them to perform the essential functions of their job and participate in the application and interview process. If you require accommodations during any part of the application process, please contact us at hr@fuelcycle.com to discuss your needs.