Astronomer Architect

Plano, Texas 75023 Posted October 19th, 2026

Role:
Astronomer Architect

Location:
Plano, TX – Onsite

Fulltime – Permanent

Job Description

Must Have Technical/Functional Skills

• Architect, deploy, and operate production-grade Airflow on Kubernetes including all components and user application dependencies, with focus on upgrades, capacity planning, HA, security, and performance tuning
• Operate a multi-scheduler ecosystem: determine when to use Airflow, distributed compute schedulers, or lightweight task runners based on workload requirements; provide unified developer experience across schedulers
• Build automation infrastructure:
  Terraform modules and Helm charts with Git Ops-driven CI/CD for environment provisioning, upgrades, and zero-downtime rollouts
• Standardize the developer experience: DAG repo templates, shared operator libraries, connection and secrets management, dependency packaging, code ownership, linting, unit testing, and pre-commit hooks
• Implement comprehensive observability: metrics collection, dashboards, distributed tracing, SLA/latency monitoring, intelligent alerting, and runbook automation
• Enable resilient workflow patterns: build idempotency frameworks, retry/backoff strategies, deferrable operators and sensors, dynamic task mapping, and data-aware scheduling
• Ensure reliability at enterprise scale: architect and tune resource allocation (pools, queues, concurrency limits) to support high-throughput workloads; optimize large-scale backfill strategies; develop comprehensive runbooks and lead incident response/postmortems
• Partner with teams across the organization to provide enablement, documentation, and self-service tooling
• Mentor engineers, contribute to platform roadmap and technical standards, and drive engineering best practices
• 5-8+ years building/operating data or platform systems; 3+ years running Airflow in production at scale (hundreds-thousands of DAGs and high task throughput).
• Deep Airflow expertise: DAG design and testing, idempotency, deferrable operators/sensors, dynamic task mapping, task groups, datasets, pools/queues, SLAs, retries/backfills, cross DAG dependencies
• Strong Kubernetes experience running Airflow and supporting services:
  Helm, autoscaling, node/pod tuning, topology spread, network policies, PDBs, and blue/green or canary strategies.
• Automation-first mindset:
  Terraform, Helm, Git Ops (Argo CD/Flux), and C/CD for platform lifecycle; policy as code (ON/Gatekeeper/Conftest) for DAG, connection, and secrets changes.
• Proficiency in Python for authoring operators/hooks/utilities; solid Bash; familiarity with Go or Java is a plus.
• Observability and SRE practices:
  Prometheus/Grafana/StatsD, centralized logging, alert design, capacity/throughput modeling, performance tuning.
• Data platform experience with at least one major cloud (AWS/Azure/GCP) and systems like Snowflake/Big Query/Redshift, Databricks/Spark, EMR/Dataproc; strong grasp of IAM, VPC networking, and storage (S3/GCS/ADLS).
• Security/compliance: SS0/OIDC, RBAC, secrets management (Vault/Secrets Manager), auditing, least privilege connection management, and change control.
• Proven incident leadership, runbook creation, and platform roadmap execution; excellent cross-functional communication.

Roles & Responsibilities

• You'll own the Airflow control plane and developer experience end-to-end-architecture, automation, security, observability, and reliability-while also evaluating and operating complementary schedulers where appropriate
• You'll build automation infrastructure and partner across data, trading, and engineering teams to deliver mission-critical pipelines at scale
• Architect, deploy, and operate production grade Airflow on Kubernetes including all components and user application dependencies, with focus on upgrades, capacity planning, HA, security, and performance tuning Operate a multi-scheduler ecosystem: determine when to use Airflow, distributed compute schedulers, or lightweight task runners based on workload requirements; provide unified developer experience across schedule Build automation infrastructure:
  Terraform modules and Helm charts with Git Ops-driven CI/CD for environment provisioning, upgrades, and zero-downtime rollouts
• Standardize the developer experience: DAG repo templates, shared operator libraries, connection and secrets management, dependency packaging, code ownership, linting, unit testing, and pre-commit hooks Implement comprehensive observability: metrics…