Senior Data Platform Engineer CR 12/11/26

Key Responsibilities
Orchestration Platform (Apache Airflow & ADF)

* 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.

Azure Resource Management

* 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).

Hands-on Data Platform evolution

* 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.

Technical Leadership

* 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).

Support & Operations (Run Team Collaboration)

* 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.

Technical Skills
6+ years of experience in data engineering, with significant time on platform or infrastructure topics
Strong technical expertise with Python and SQL (advanced), including PySpark and Delta Lake semantics
Deep hands-on experience with Apache Airflow in production: DAG design, deployment, operations, upgrades, and orchestration of Databricks and ADF workloads
Hands-on experience with Azure resource management:
Key Vault, managed identities and service principals, secret management
Access management: Azure RBAC, Entra ID, Unity Catalog permissions
Azure DevOps (repos, pipelines, service connections, environments) and CI/CD for data platforms
ADLS, networking and security basics, Azure Data Factory
Infrastructure as Code (Terraform) and multi-environment promotion
Hands-on experience with Databricks and Unity Catalog (compute, jobs, governance)
Observability and monitoring, alerting and incident management
Solid understanding of Data Lakehouse architecture, Medallion architecture and metadata-driven architecture; awareness of cost management and performance
Soft Skills
Strong problem-solving and analytical thinking
Ability to work in cross-functional teams and to influence without authority
Ownership mindset and attention to detail
Good communication skills with both technical and business stakeholders: leads with the recommendation, quantifies, and is transparent about uncertainty