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.

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 .

• 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

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