Job Description

Position:- Astronomer Architect

Location :- Plano, TX (Onsite)

Duration:- Fulltime

Job Description

We're seeking a seasoned engineer to design, operate, and scale our workflow orchestration platform with a primary focus on Apache Airflow. You'll own the Airflow control plane and developer experience end-to- end--architecture, auto 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 minion-critica 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 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

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