Staff Data Engineer, Payabli

• 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
• 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
• 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 an instinct for keeping the blast radius of mistakes small
• 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
• Your skills and trajectory matter more than checking every box

Desirable

• 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
• If you're energized by building it right the first time rather than untangling it later, this is a rare seat
• 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. You're setting the bar, not just clearing it