Senior Data Platform Engineer

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.

Must Have

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.