Data Platform Engineer, Shift4

• 5+ years in platform, infrastructure, or DevOps roles supporting data-intensive systems in production
• Deep hands-on AWS experience: ECS/Fargate (task sizing, autoscaling, deployments), SQS (throughput, DLQs, visibility timeouts, batching), S3, IAM, CloudWatch, and Aurora/RDS from the operator side
• Proven track record tuning message-driven workloads: consumer concurrency, backpressure, retries, idempotency, throughput versus cost
• Experience operating pipelines into Snowflake from the ingestion side (Snowpipe/COPY, stages, load monitoring)
• Infrastructure as code with Terraform/Ansible, containerization with Docker, and CI/CD pipelines
• Observability and alerting for pipeline health and lag using Prometheus/Grafana, CloudWatch, Datadog, or the Elastic Stack
• Enough Python to read, debug, and instrument worker code
• Passion for automation, performance, and clean system design

Desirable

• Shift4 is seeking a Data Platform Engineer to join our Data Platform team and build, maintain, and optimize the pipelines that move data from our production systems into our analytics warehouse.
• Today, our infrastructure uses Amazon ECS services and SQS queues to replicate data from Aurora MySQL into Snowflake, with plans to expand to additional data sources and consumers as our platform evolves
• This is a hands-on infrastructure engineering role focused on pipeline performance, reliability, scalability, resource optimization, cost efficiency, and observability.
• You will help ensure data is delivered accurately, reliably, and on time as our data platform grows.
• Data modeling, warehouse cost management, and business transformation logic are managed by partner teams, allowing you to focus on the infrastructure and systems that power our data ecosystem—all while embracing the Shift4 way
• Operate, tune, and scale the ECS/SQS-based ingestion pipelines from Aurora MySQL into Snowflake: task sizing, autoscaling, consumer concurrency, batching, retries, and dead-letter handling
• Own pipeline reliability: define SLOs for freshness and lag, build alerting and dashboards, lead incident response and root-cause analysis for pipeline failures
• Build and maintain infrastructure as code, deployment automation, and CI/CD for pipeline services
• Continuously benchmark and tune throughput versus cost across ECS, SQS, and Snowflake ingestion (Snowpipe/COPY, stages)
• Onboard new sources and consumers to the platform in partnership with application, data, and analytics teams
• Apply data-quality and security controls appropriate for PCI-compliant or payment-handling environments: encryption, secrets handling, least-privilege access, audit trails
• Proactively explore and apply AI tools to improve efficiency across the pipeline lifecycle (development, testing, runbooks, and documentation)