Senior Data Platform / Airflow Engineer
LTM
Role description
- Design, build, and operate large-scale data platforms powered by Apache Airflow
- Manage and optimize hundreds to thousands of DAGs with high task throughput
- Drive platform reliability, scalability, and performance improvements
- Own end-to-end lifecycle: deployment, monitoring, incident response, and roadmap execution
- Collaborate with cross-functional teams to enable robust, secure, and efficient data workflows
- Strong hands-on experience with DAG design, testing & idempotency
- Deep knowledge of sensors, deferrable operators, dynamic task mapping, SLAs, retries, backfills
- Experience with cross-DAG dependencies, task groups, datasets, pools, and queues
- Experience running Airflow on Kubernetes (Helm, autoscaling, tuning)
- Understanding of network policies, PDBs, and deployment strategies (blue-green / canary)
- Automation-first mindset with Terraform, Helm, GitOps (Argo CD / Flux), CI/CD
- Strong Python skills for DAGs, operators, and utilities
- Familiarity with Bash; exposure to Go or Java is a plus
- Hands-on with Prometheus, Grafana, StatsD, and logging systems
- Experience with capacity planning, performance tuning, and reliability practices
- Experience with Azure (preferred) or any major cloud
- Exposure to Snowflake, BigQuery, Redshift, Databricks, Spark, EMR/Dataproc
- Solid understanding of IAM, VPC networking, and storage (S3/GCS/ADLS)
- Knowledge of SSO/OIDC, RBAC, secrets management (Vault/Secrets Manager)
- Strong focus on auditing, least privilege, and governance
Vacancy posted 10 hours ago
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