Data Engineer

About Sapiom

AI agents are beginning to act on behalf of companies and users — making purchases, spinning up compute, triggering workflows, and interacting with third-party systems. But today's financial infrastructure was built for humans, not autonomous systems. Companies and developers need a way to give agents controlled access, meter actions, monetize usage, and transact across rails, without rebuilding payments, risk, and compliance internally.

Sapiom builds the financial payments infrastructure for the machine economy - autonomous spend rails that enable AI agents to transact with real-world services safely, processing every dollar spent, every policy decision navigated, and every risk signal generated.

We have assembled a world-class team with deep payments and infrastructure DNA to build the operating system for machines. Backed by a $15.75M investment from Accel, Menlo, and Anthropic, we are moving with relentless focus to deploy the economic substrate for autonomous agents.

About the Role

This is a foundational infrastructure role at a company where the data layer isn't a back-office function — it's the nervous system of a payments platform processing every agent transaction, policy decision, and risk signal in real time. The right person thrives on ownership, has strong opinions about data quality and governance, and moves with the urgency of someone who knows that bad data costs more than bad code. As an early data engineer, you'll define not just the pipelines but the standards, architecture, and culture of data at Sapiom.

What You Will Do

You'll own Sapiom's data infrastructure end-to-end — designing and scaling ETL pipelines, defining schemas that survive 10x growth, and building the governance and quality frameworks that make data trustworthy across the company. You'll architect standardized data models that enable self-serve AI-powered insights, giving Analytics, Data Science, and product teams the visibility they need to move fast without coming to you for every query. The mandate is broad: pipelines, quality, security, observability, and the cross-functional partnerships that keep it all running.

Responsibilities

• Build, scale, and optimize production-quality ETL pipelines — owning the full lifecycle from ingestion through availability, with clear quality and SLA standards

• Design data schemas and architect for scale — anticipating 10x data growth and building models that don't require rework when it arrives

• Own data quality, governance, security, and schema design across the platform — setting the standards and making sure they hold

• Develop standardized, self-serve data models that enable AI-powered analytics — reducing friction for partner teams and eliminating one-off data pulls

• Instrument pipeline observability and surface key health metrics to Analytics, Data Science, and DevOps — proactively surfacing issues before they become incidents

• Partner closely with Data Science, Analytics, and DevOps — operating as a force multiplier across teams, not a bottleneck

Requirements

• Demonstrated track record — 5+ years — transforming raw data into governed, well-documented, production-ready datasets that business teams can trust and use

• Deep hands-on experience building and deploying production data pipelines using SQL, Python, Spark, AWS Glue, EMR, DBT, and Airflow

• Strong command of MPP databases — Snowflake, AWS Redshift, or Teradata — with 3+ years of hands-on production use

• Proven partnership record with Engineering, Analytics, Data Science, and DevOps teams — someone who treats cross-functional relationships as core to the job, not peripheral to it

• Architectural instincts — able to design schemas and systems that scale gracefully, not just handle today's load

• Comfort operating in an on-call rotation — including incident response outside regular working hours when the pipeline demands it

• Clear communicator who can translate complex data infrastructure decisions into plain-language insights for both technical and non-technical stakeholders