Senior Data Infrastructure Engineer, Aircall

• 4+ years (Senior: 6+) in data engineering, data platform or infrastructure engineering
• Strong Python and SQL, with demonstrated experience building frameworks and tooling others depend on, not only pipelines
• Production experience with an orchestration framework (Airflow, Dagster, Prefect) at meaningful scale — including the operational side, not just DAG authoring
• Hands-on Apache Spark and distributed-systems fundamentals
• Deep AWS experience (S3, EKS/ECS, IAM, Glue/Athena or equivalent)
• Comfortable building and debugging CI/CD, infrastructure as code (Terraform) and GitOps workflows; familiar with Kubernetes and Docker
• Track record owning reliability: SLAs, monitoring, alerting, on-call, and post-incident hardening
• Daily, hands-on use of AI coding tools (Claude Code, Cursor, or equivalent) as a core part of how you build and operate infrastructure
• Great cross-functional communication — you'll shape data contracts with backend engineering and align expectations with analytics consumers

Desirable

• Aircall's Data team is mid-migration: we are moving off a single Redshift cluster onto an Apache Iceberg lakehouse on S3, with Flink CDC into Kafka for ingestion and dbt-on-Spark via Apache Kyuubi on EKS for transformation
• It's a real greenfield platform build — already scoped and underway — on top of a stack that carries ten years of startup-growth history, and all the quirks that come with it
• We're building this role to give platform work the runway it deserves
• Right now, our engineers wear two hats — owning the infrastructure and the business datasets running on top of it — and we're ready to invest in the high-leverage frameworks that will make both jobs easier: data quality automation, schema registry, and staging and gated promotion
• This is a dedicated platform seat: your chance to build those foundations from the ground up
• Your customers are the analytics engineers, data scientists, and AI agents who build on what you ship, and your product is their leverage
• Build and operate the lakehouse: Apache Iceberg on S3, table design and maintenance, partitioning and compaction, and the migration of remaining Redshift workloads onto it
• Own ingestion end to end — Flink CDC → Kafka (MSK) → Iceberg, plus Rudderstack, Fivetran and DMS sources — and hold the freshness and reliability SLAs on it
• Run and evolve the compute and orchestration layer: Apache Kyuubi on EKS for dbt-spark, Airflow (completing its ECS → EKS migration), autoscaling, spot strategy and cost efficiency
• Build the tooling, libraries and templates that let analytics engineers and data scientists own their own pipelines without filing a ticket — self-service is the deliverable, not a side effect
• Close our environment gaps: a real staging environment, CI that tests against staging rather than production, automated schema-change detection, gated promotion and canary deploys for critical models
• Own governance and access at the platform level: Lake Formation row/column RBAC, StrongDM zero-trust access, SSO, audit logging, and PII handling
• Own observability: Monte Carlo, lineage, alerting and the SLAs we publish — and drive incidents to root cause and to a durable fix
• Champion infrastructure as code and automation (Terraform, GitLab CI, GitOps) across everything the team runs
• You own the platform: ingestion, storage, orchestration, compute, access control, observability and the frameworks on top of them. Our Analytics Engineers own the business-facing layer — dbt models, golden datasets, metric definitions and the semantic layer — and consume your platform as a service. You're energized by multiplying other people's speed, and drawn to problems where the end user is a fellow engineer