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)
Qualifications
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
Nice to have
CDC tooling (Debezium, AWS DMS) and MySQL Binlog replication
Message brokers beyond SQS: Apache Kafka/Confluent, RabbitMQ
Elastic Stack or time-series database (Prometheus, InfluxDB) administration
Experience in a PCI or otherwise regulated environment
Payments or fintech domain knowledge
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