Senior Data Engineer

Aurora Payments | Full Time | Remote (Western Hemisphere)

The Short Version

AI agents are starting to shop, select, and pay on behalf of buyers. The processors that can offer payment trust, commerce orchestration, and an agent-accessible platform will own the next decade. Few are close.

Aurora is building all three. You would build the data platform underneath it.

This is not a backfill. It is the founding technical hire for a new data engineering function. Small team, full ownership, AI-native tooling. The architecture scales without heroics or it doesn't ship.

About Aurora Payments

Aurora Payments is a payments infrastructure company. We sit between processors, merchants, and the agents that will transact on their behalf. Since 2005, we've been empowering over 27,000 businesses and processing $12B+ annually through our ARISE platform. We specialize in payment processing, embedded commerce, zero-cost processing, and comprehensive payment infrastructure that gives our partners and merchants control, flexibility, and modern tools to grow their revenue.

The Role

We are building a unified data platform from scratch — ingestion through serving — across multiple payment processors with different file formats, fee structures, and delivery mechanisms. Today that platform does not exist. There is no unified pipeline, no transaction-level lineage, no self-service reporting. You would build it.

You ship across the full stack. No artificial boundaries between "pipeline engineer" and "analytics engineer." You parse the raw files, model the data, validate the output, and hand a clean contract to the team building the frontend. Data engineering is the home base, not the ceiling. Small team, full ownership, AI-native tooling. The architecture scales without heroics or it doesn't ship.

What You'll DoFirst 30 Days

• Extend an existing Dataform project in BigQuery (staging models, data quality checks)
• Build reconciliation proofs against production data
• Stand up ingestion for a new transaction-level source (GCS to BigQuery, PCI scrubbing required)

Next Phase

• Own transaction-level lineage across multiple payment processors
• Build automated ingestion for processor files and APIs
• Design the journaling and audit layer (point-in-time recalculation, not just snapshots)
• Deliver the data contract consumed by the merchant-facing dashboard

Beyond MVP

• Build a processor-agnostic onboarding framework (we acquire companies; their data has to flow in)
• Evolve into tech lead as the team grows

What We're Looking ForRequired Qualifications

Data modeling depth.
The top differentiator for this role. The domain has tricky grain — merchant-level aggregates that hide fee-category detail, mid-month adjustment conventions that silently break date filters, classification errors that affect 61% of rows. You should be able to look at a model and say "this grain is wrong" before it ships. Dimensional modeling, slowly changing dimensions, knowing when to denormalize. Demonstrated in production, not in theory.

You ship.
First deployed artifact within 30 days. We have a Dataform project scaffolded, BigQuery sources defined, a first data quality assessment complete, and data waiting. The work has started. Comfort with incomplete specs — documentation is being created in parallel with the build. Some answers only come from querying the data. Default to building, not waiting.

Advanced SQL + Dataform + BigQuery.
Advanced SQL is the daily language: window functions, CTEs, cross-grain aggregation. Experience with Dataform and BigQuery or comparable technologies required. Understands materialization strategies, incremental models, and testing frameworks.

AI-native workflow.
You work daily with Claude Code, Cursor, Copilot, or equivalent. Aurora leans heavily on AI (mostly Claude) , we use it to write, test, debug, and explore. This is a two-person team where both members operate at 3-5x through AI leverage. AI fundamentally shapes how we work across the stack. A traditional workflow at traditional speed is a mismatch.

Nice to Have

• Financial data or payments domain experience (residuals, interchange, fee structures, commission calculations)
• Pipeline construction from raw files or APIs (schema drift, late-arriving data, file format inconsistencies)
• PCI-DSS awareness — you know why you hash before you load
• Python for light scripting

Not required:
Frontend development, data science/ML, GCP platform administration, management experience.

Working Environment

Two-person data team with high autonomy. You coordinate with the data architect who owns GCP infrastructure, the ops SME who runs residuals today, and the product and engineering team building the end-user features. No Airflow/Dagster yet — orchestration choices are yours to influence.

The source system is a black box. Intermediate transformations are not visible. You work from outputs backward when inputs are not accessible. Ambiguity is the norm, not the exception.

Weekly artifacts. Incremental proofs, not big-bang releases. Comfort showing work in progress to stakeholders who are not technical. Tools: Dataform, BigQuery, GCS, Claude Code, Git, Sigma.