Astronomer Architect

Plano, Texas 75023 Posted October 9th, 2026 ##### **Job Type: Full Time** ##### **Job Category: IT** #### **Job Description** **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 GitOps-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, GitOps (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/BigQuery/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 Ma nager), 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 GitOps-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 **Nice To Have :-** * Experience leading migrations to/from Airflow * Open Lineage/Marquez adoption; Great Expectations or other date quality frameworks; data contracts. * dbt Core/Cloud orchestration patterns (state management, artifacts, slim Ci). * Cost optimization and capacity planning for schedulers and workers; spot instance strategies. * Multi-region HA/DR for Airflow metadata DB; backup/restore and disaster drills. * Building internal developer platforms/portals (e.g., Backstage) for self-service pipelines. * Contributions to Apache Airflow or provider packages; familiarity with recent AlPs/Airflow 2.7+ features ##### **Required Skills** Mobile Architect