Senior Data Platform Engineer CR 12/11/26

Own the Apache Airflow platform: environments, deployment, upgrades, configuration, connections, variables, pools and DAG standards.

Work on the migration from Azure Data Factory to Airflow and the evolution of the Metadata-Driven Orchestration Framework (DAG generation, reusable operators, dependency and scheduling patterns).

Ensure Airflow is reliable and observable: SLAs, alerting, retries, backfills, health monitoring and clear failure ownership.

Manage Azure resources with Infrastructure as Code (Terraform): Databricks workspaces, Data Lake Storage, Data Factory, Key Vault, networking and supporting services.

Own secrets and identity management: Key Vault, service principals and managed identities, secret scopes and rotation.

Own access management: Azure RBAC, Entra ID groups, Unity Catalog grants and ownership, following least privilege, plus access requests and periodic reviews.

Own Azure DevOps: repositories, CI/CD pipelines, branch policies and multi-environment promotion, including deployment validation and Terraform state.

Track cost and governance of Azure resources: tagging, budgets, right-sizing and cost attribution (e.g. query tagging).

Contribute to the implementation of the Data Catalog and Data Quality Framework and it’s lineage across the platform.

Drive observability: pipelines that are observable, debuggable and alert-driven, with clear ownership of failures.

Operate the Databricks platform: Unity Catalog structure, cluster and job policies, Auto Loader ingestion patterns, and performance and cost optimization of workloads.

Participate in architecture reviews and technical decision-making, stating trade-offs (performance, cost, reliability, complexity) with data.

Define and promote engineering standards: naming conventions, patterns and guidelines, through code reviews and design reviews.

Collaborate with Architects, Product Owners and data engineers in technical maturation of data products.

Build and maintain developer tooling and automation, including AI-assisted engineering workflows (shared agent instructions, skills, automated PR review).

Provide second-line support to the Run/Operations team for complex production topics.

Troubleshoot and resolve platform incidents, failures and performance issues; lead root cause analysis and post-mortems.

Ensure proper handover, documentation and operational readiness of platform changes.

Continuously improve runbooks, monitoring and operational processes.