Senior Data Platform Engineer CR 12/11/26

We are looking for a Senior Data Platform Engineer to help drive the evolution of our Azure-based Data Platform and Lakehouse architecture .

This role is focused on the platform itself . The professional will own and evolve the foundations that data product teams build on: Apache Airflow orchestration (migrating from ADF) , Azure resource management (Key Vault, identity and access, networking, storage), Azure DevOps CI/CD and Infrastructure as Code , Databricks and Unity Catalog governance , observability and cost efficiency , while remaining hands-on and close to the teams that use the platform.

Key Responsibilities
Orchestration Platform (Apache Airflow & ADF)

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

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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).

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

Azure Resource Management

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

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

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

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

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

Hands-on Data Platform evolution

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

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

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

Technical Leadership

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

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

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

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

Support & Operations (Run Team Collaboration)

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

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

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

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Continuously improve runbooks, monitoring and operational processes .