Senior Data Engineer

Super Payments is the only global fintech platform providing 0% processing fees for merchants. Our mission is to use data and AI to make payments free for businesses, so that everyone wins
.

At Super, we are disrupting payments, like Spotify did to music and Robinhood did to trading. We don't make money from transaction fees; instead, we monetise through value-added services such as lender commissions and FX. Our platform supports multiple payment methods — from traditional cards to direct bank transfers via open banking, as well as Apple Pay, Google Pay, and our own Buy Now Pay Later solution.

Super has raised $66M from leading investors including Accel, Union Square Ventures and Local Globe (the same investors behind Spotify, Stripe, Monzo) and founded by Samir Desai CBE, co-founder of Funding Circle.

Already trusted by thousands of businesses and more than 4 million customers
, Super is processing at a run rate of £1.5B and growing 4x YOY

Our Values

• Customer obsessed: We only succeed when our customers do.
• Move fast
  :
  Build, test and improve quickly. Progress matters more than perfection.
• Own it
  :
  Be accountable, solve problems, and make it happen.
• Be open
  :
  Act with honesty and respect. Transparency builds trust.
• Win together
  :
  Collaboration beats ego every time.

Role Overview

We run an event-driven data platform on AWS and Snowflake. Events flow from our product services through Event Bridge, Kinesis Firehose, S3, and are auto-ingested into Snowflake via Snowpipe; batch and third-party data lands through Snowpark-deployed Python procedures. All infrastructure is defined in Terraform/Terragrunt. Python (Poetry, Pydantic, pytest) is our pipeline language;
Git Hub Actions runs CI/CD. Datadog gives us end-to-end pipeline observability.

Key Responsibilities

• Event Pipeline Ownership: Build and evolve the Event Bridge → Firehose → S3 → Snowpipe ingestion path, including partitioning and throughput tuning as event volumes grow across streams like fraud, payments and merchant.
• Batch & Third-Party Integrations: Design and deploy Snowpark-based Python procedures for file-based and third-party data loads, scheduled and monitored as Snowflake Tasks.
• Schema Evolution: Work with product engineers to manage upstream schema changes and late-arriving data without breaking downstream consumers.

Infrastructure & Reliability

• Infrastructure as Code: Own and extend Terraform/Terragrunt modules for ingestion infrastructure across multiple environments.
• Pipeline Observability: Design Datadog monitors and alerting for Firehose, Event Bridge, DLQs and Snowpipe; triage and resolve ingestion failures.
• Security & Compliance: Manage PII handling (hashing, secure deletion), masking, data governance and access in Snowflake. Support audit-ready, reconciled data for Finance and Compliance.
• Dbt: Build privacy-first data pipelines using dbt and Snowflake Dynamic Tables to safely expose sensitive data for downstream analytics and reporting.
• Data Quality: Build testing and monitoring for ingestion and transformation logic to catch issues before they reach the business or the Analytics Engineering team.
• Stakeholder
  
  Collaboration:
  
  Partner with the wider Engineering team and Analytics Engineering team as a downstream consumer, to ensure pipelines deliver accurate, audit-ready data.

Requirements

We'd love it if you have

• AWS & Event-Driven Architecture: Hands-on experience with streaming/event ingestion (Kinesis, Event Bridge or equivalents), S3 partitioning, and queue/DLQ patterns.
• Infrastructure as Code: Terraform experience required;
  Terragrunt a plus. Comfortable owning modules across environments, not just consuming them.
• Snowflake & Warehouse Engineering: Strong SQL for Snowflake, including Snowpipe, Tasks and Snowpark.
• Dbt
  :
  Proven experience in building well tested SQL and python models in dbt.
• CI/CD & Data Security: Experience with Git Hub Actions (or equivalent), and secrets/key management, PII and sensitive data handling in Snowflake.
• Python Coding: Python skills for pipeline and infrastructure code (typed models, testing, CI-deployed services), not just scripting or analysis.

Technical Communication: A pragmatic, systems-level mindset with the ability…