Capitole is growing fast, and we want you to be part of the journey

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We are looking for a Senior Data Platform Engineer with +6 years of experience 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.

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 .

- 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

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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.





- 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

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- 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

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- 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)

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- 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 .

Requirements:
Technical Skills

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- 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 :

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- Key Vault , managed identities and service principals, secret management

- Access management : Azure RBAC, Entra ID, Unity Catalog permissions

📌 Hibrido BCN (Barcelona)
🏢 Capitole
📍 Barcelona