Senior Data Engineer

Referment is working with a fast-growing fintech that is building a zero-fee payments platform for merchants, funded by well-known venture investors and already processing at a significant run rate with millions of customers. You'll join a data team that runs an event-driven platform on AWS and Snowflake. Product services emit events through EventBridge and Kinesis Firehose into S3, auto-ingested into Snowflake via Snowpipe, while batch and third-party data lands through Snowpark-deployed Python procedures. All infrastructure is defined in Terraform/Terragrunt, pipelines are built in Python, CI/CD runs on GitHub Actions, and Datadog provides end-to-end pipeline observability. The Role

• Own and evolve the event ingestion path — EventBridge to Firehose to S3 to Snowpipe — including partitioning and throughput tuning as event volumes grow across streams such as fraud, payments and merchant.
• Design and deploy Snowpark-based Python procedures for file-based and third-party data loads, scheduled and monitored as Snowflake Tasks.
• Work with product engineers to manage upstream schema changes and late-arriving data without breaking downstream consumers.
• Own and extend Terraform/Terragrunt modules for ingestion infrastructure across multiple environments.
• Design Datadog monitors and alerting for Firehose, EventBridge, DLQs and Snowpipe, and triage ingestion failures when they occur.
• Manage PII handling, masking, data governance and access in Snowflake, supporting audit-ready reconciled data for Finance and Compliance.
• Build privacy-first data pipelines using dbt and Snowflake Dynamic Tables, with testing and monitoring that catches issues before they reach the business or the Analytics Engineering team. What We're Looking For
• Hands-on experience with streaming and event ingestion on AWS — Kinesis, EventBridge or equivalents — plus S3 partitioning and queue/DLQ patterns.
• Terraform experience is essential; owning modules across environments rather than just consuming them. Terragrunt is a plus.
• Strong SQL on Snowflake, including Snowpipe, Tasks and Snowpark.
• Proven dbt experience building well-tested SQL and Python models.
• Experience with GitHub Actions (or an equivalent) and with secrets management, PII and sensitive data handling in Snowflake.
• Python skills for pipeline and infrastructure code — typed models, testing and CI-deployed services rather than just scripting. You'll need a pragmatic, systems-level mindset and the ability to explain pipeline failures, infrastructure trade-offs and data-flow issues clearly to both engineers and non-technical stakeholders. This role suits someone who takes full ownership of what they build, from design through to production reliability. This could suit a data engineer at a scale-up or a data-heavy fintech who has outgrown ticket-driven work and wants to own an entire ingestion platform end to end.