Staff Data Engineer

Join Payabli as the founding Data Engineer for our Data Engineering team. In this role, you will make foundational decisions that will shape the future of our payments data architecture and pipeline. You will be responsible for architecting the platform, building pipelines, modeling data, ensuring reliability and accuracy, and enabling AI/ML and analytics. You will also set the standard for data practices and tooling. This is a unique opportunity to build something from the ground up and have a lasting impact on the company.

• Architect the platform, set the warehouse/lakehouse direction, and establish the data lake and layered architecture.
• Design and run batch and streaming pipelines that move data reliably out of production systems, ensuring data quality and observability.
• Define the canonical datasets and models the whole company depends on, ensuring correctness and compliance with regulations.
• Experience with pipeline orchestration (Airflow, Dagster, Prefect, or equivalent) and large-scale processing (such as Spark)
• A high technical bar set through influence and example. You make the work and the people around you better, and you're as comfortable in the codebase as you are in a design review
• Experience working with sensitive or regulated data - access controls, encryption, governance, and an instinct for keeping the blast radius of mistakes small
• Production experience on a major cloud (AWS, GCP, or Azure), including security and cost patterns
• Expert SQL and strong Python
• Strong data modeling skills - dimensional, normalized, or Data Vault - and a sense for designing models that age well
• Deep experience in at least one modern lakehouse/warehouse ecosystem - for example Snowflake with dbt and Fivetran, or Databricks with Spark, Delta Lake, and Unity Catalog. We care that you've gone deep somewhere and can reason from first principles across stacks, not that you've used a specific product
• 8+ years building production data systems, with a track record of owning architecture and seeing big decisions through to production
• 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)
• An interest in growing into people leadership as the function scales. We expect technical leadership from this role from the start; whether you want to manage a team down the road is genuinely up to you, and there's a clear path if you do
• Your skills and trajectory matter more than checking every box
• Experience supporting ML/AI workloads - feature stores, training/inference pipelines, MLflow