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Astronomer Architect

Location

: Plano, TX

Experience

: 10 - 15 Years

Apply By

: 7th December 2026

Job Description

Must Have Technical/Functional Skills

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

TCS Employee Benefits Summary:

Discretionary Annual Incentive.

Comprehensive Medical Coverage:

Medical & Health, Dental & Vision, Disability Planning & Insurance, Pet Insurance Plans.

Family Support:

Maternal & Parental Leaves.

Insurance Options:

Auto & Home Insurance, Identity Theft Protection.

Convenience & Professional Growth:

Commuter Benefits & Certification & Training Reimbursement.

Time Off:

Vacation, Time Off, Sic k Leave & Holidays.

Legal & Financial Assistance:

Legal Assistance, 401K Plan, Performance Bonus, College Fund, Student Loan Refinancing.

Salary Range:

$100,000-$120,000 a year

Location

Plano, TX

Function

TECHNOLOGY

Role

Technical Architect

ID

435537

Skills

AWS | Azure

Salary Range

$100,000 - $120,000

Desired Candidate Profile

Qualifications

BACHELOR OF COMPUTER

SCIENCE