Staff Data Engineer - Payabli

About Payabli

Payabli is a next-generation Payments Infrastructure and Monetization Platform purpose-built for vertical software companies. Through a single, developer-friendly API with low-code embedded payment components, Payabli enables platforms to seamlessly embed, monetize, and operationalize payments—making payments a core part of their platform and business model.

By unifying payment acceptance, payment issuance, and advanced payment operations tooling, Payabli empowers software companies to manage and move money through a single infrastructure stack that delivers total control over the payments experience. Built to scale with PCI DSS 4.0 and SOC 2-compliant security, Payabli’s infrastructure delivers enterprise-grade reliability and trust while leveraging AI-driven intelligence to enhance visibility, streamline operations, and drive revenue growth.

Backed by leading fintech investors including QED Investors, Fika Ventures, TTV Capital, and Bling Capital, Payabli is setting the standard for embedded payments infrastructure powering the next generation of vertical SaaS.

Role Overview

This is the founding Data Engineer for the Data Engineering team at Payabli. You won't inherit an existing architecture or a pipeline graph someone else built—you'll make the foundational, one-way-door decisions that define how we model, move, and trust payments data for years to come: the warehouse and lakehouse direction, how we model payments data, how we keep sensitive financial data safe, and what "good" looks like for every data engineer who follows you.

The leverage is the point. The choices you make in your first quarter will still be load-bearing years from now, and you'll be the technical foundation beneath our analytics, ML, and AI ambitions.

What You'll Do

• Architect the platform: Set our warehouse/lakehouse direction and stand up the data lake and layered architecture that turns our raw system of record into trustworthy, queryable, intelligence-ready data.
• Build the pipelines: Design and run batch and streaming pipelines that move data reliably out of our production systems—CDC, ELT, and real-time where it matters.
• Model the data: Define the canonical datasets and models the whole company depends on, getting the grain, semantics, and contracts right.
• Own reliability and accuracy: This is financial data, so correctness is non-negotiable. You'll own data quality, observability, integrity checks, and the testing and monitoring that let us trust it.
• Build for a regulated environment: Design in role-based access, masking, lineage, and auditability from day one, and keep sensitive financial data out of places it doesn't belong.
• Enable AI/ML and analytics: Build the feature pipelines and trustworthy data foundation our intelligence work relies on, moving us from systems of record toward systems of intelligence and action.
• Set the standard: Establish the practices, tooling, and CI/CD for data that the future team inherits.

What We're Looking For

• 8+ years building production data systems, with a track record of owning architecture and seeing big decisions through to production.
• Expert SQL and strong Python skills.
• Deep experience in at least one modern lakehouse/warehouse ecosystem (e.g., Snowflake with dbt and Fivetran, or Databricks with Spark, Delta Lake, and Unity Catalog).
• Strong data modeling skills (dimensional, normalized, or Data Vault) and a sense for designing models that age well.
• Experience with pipeline orchestration (Airflow, Dagster, Prefect, or equivalent) and large-scale processing (such as Spark).
• Production experience on a major cloud (AWS, GCP, or Azure), including security and cost patterns.
• Experience working with sensitive or regulated data—access controls, encryption, governance, and keeping the blast radius of mistakes small.
• A high technical bar set through influence and example.

Nice to Haves

• Payments, fintech, or other regulated-domain experience, including familiarity with PCI DSS and tokenization/vaulting patterns.
• Streaming infrastructure (Kafka, Kinesis, Flink).
• Data governance, lineage, and observability tooling (Unity Catalog, Snowflake Horizon, Monte Carlo, Great Expectations, OpenLineage).
• Experience supporting ML/AI workloads—feature stores, training/inference pipelines, MLflow.
• An interest in growing into people leadership as the function scales.

What We Offer

• Competitive salary
• Stock options with the potential to unlock more equity as we grow
• Flexible PTO and paid parental leave
• Medical, dental, & vision insurance
• 401(k), HSA, pre-tax savings programs