Lead Data Engineer

~5+ years of data engineering experience with increasing scope and ownership Strong background with AWS (RDS, Redshift, S3, Lambda, IAM) and Python Deep experience building and maintaining scalable ETL/ELT pipelines, data models, event schemas, and orchestration Strong command of SQL, warehousing concepts, dimensional modeling, and performance optimization 0→1 and Scale Experience Have built or significantly overhauled a data platform at a high-growth startup Comfortable owning the entire data ecosystem without existing mature processes or structure Cross-Functional Partner & Communicator Able to translate ambiguous business questions into clear, prioritized technical work Experienced in partnering with non-technical stakeholders Ownership & Autonomy High sense of ownership and accountability Bias toward action, with strong judgment

Own and evolve the Benepass Data Platform Serve as the primary architect and builder of our data infrastructure across our AWS stack Evaluate and lead the evolution of our data stack Define ingestion, modeling, testing, and monitoring frameworks Deliver Business-Impacting Data Capabilities Partner with various teams to understand data needs and deliver high-value pipelines Support financial and people analytics, revenue and margin reporting, operational workflows, and compliance/audit needs Provide tooling, infrastructure, and best practices Hands-On Technical Ownership Design and implement ETL/ELT pipelines, schemas, and transformations in our AWS environment Debug complex data issues Establish standards for data reliability, testing, documentation, privacy, and governance Set Vision & Roadmap for Data Engineering Create clarity where none exists—define architectural direction, roadmap, SLAs, data contracts, and cross-team processes Anticipate scaling needs Influence product and engineering teams on event instrumentation, schema design, and logging Prepare for Future Team Growth (Without Immediate Hiring) Define the future data engineering team structure Establish foundational patterns, tooling, and documentation Position Benepass to scale efficiently