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
Job Tags
Permanent employment, Full time