Remote Senior Data Engineer

About Fuel Cycle : Fuel Cycle empowers leading organizations with agile research solutions that deliver decision-ready insights — fast, flexible, and fully integrated. As a market research disruptor, our AI-powered Insights Platform is built for speed, precision, and scale. With cutting-edge tools and seamless audience connectivity, we help brands ditch the guesswork and make smarter, customer-led decisions at lightning speed.

Why work at Fuel Cycle? Join a high-growth team where curiosity is valued, ownership is encouraged, and your work drives real-world impact. Whether you’re based at our Los Angeles HQ, New York City hub, working remotely across the U.S., or part of our global team in India, you’ll help shape the future of decision intelligence for some of the world’s most iconic brands.

Overview: 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.

Key Responsibilities: Data Lake 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 Preferred, but Not Required: 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 Benefits