Staff Data Engineer

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. 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

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.

• 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.

• 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.

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.
• 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.

• 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.

• 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.

• Highlight any end‑to‑end data pipelines you built that integrate payment transaction feeds (e.g., webhook, CSV, or Excel uploads) into a data lake or warehouse, and include a short repo or notebook link showing how you handled PCI‑DSS 4.0/SOC 2 masking, tokenization, and audit trails—Payabli’s interviewers will probe your security‑first mindset.
• Prepare a remote‑ready portfolio that showcases at least one project where you transformed large Excel workbooks (pivot tables, Power Query, VBA/macros) into automated ETL jobs using Python/SQL or Airflow. Annotate the code with comments on performance tuning and cost‑optimisation for cloud environments (e.g., Snowflake, BigQuery).
• In your cover letter and interview answers, explicitly reference Payabli’s low‑code payment components and describe how you would design a data model that supports real‑time analytics for “embedded payments” while preserving referential integrity across merchants, payouts, and refunds. Use concrete metrics (e.g., latency < 200 ms, 99.9 % data freshness).
• Practice system‑design questions that center on scalable, compliant payment data flows: be ready to sketch a pipeline that ingests high‑velocity transaction streams, applies enrichment via Excel‑derived lookup tables, and writes to an auditable data mart. Emphasize partitioning, encryption at rest/in‑flight, and monitoring with alerts for anomaly detection.
• When negotiating salary, pull the latest remote Staff Data Engineer compensation data for fintech SaaS firms (e.g., Levels.fyi, Hired) and position your ask around the median + 15 % range, citing your proven experience with payment‑grade security, AI‑driven analytics, and large‑scale Excel automation that directly reduces Payabli’s data‑processing costs.