Senior Data Platform Engineer

MARGO

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

Posted 11/11/2026full-timeRemote • PolandSenior💰 PLN 180 - PLN 200 per hourWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in managing Apache Airflow and Azure resources, with a strong focus on data engineering, orchestration, and observability. Proficient in Python, SQL, and Terraform, with a solid understanding of Data Lakehouse architecture and cost management.

Highest-signal resume keywords

Apache Airflow ManagementPython ProgrammingAzure Resource ManagementTerraform Infrastructure as CodeCI/CD with Azure DevOps

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonSQLApache AirflowTerraformDatabricksAzure Data FactoryAzure RBACEntra IDPySparkDelta Lake

Soft Skills

Problem-SolvingAnalytical ThinkingCross-Functional CollaborationAttention to DetailGood Communication

Tools & Technologies

Azure DevOpsKey VaultUnity CatalogData Lake StorageNetworking

Industry Keywords

Data EngineeringMetadata-Driven ArchitectureData Lakehouse ArchitectureObservabilityIncident Management

Tech Stack

Tools & technologies

AirflowApacheAzurePySparkPythonSQLTerraformUnityVault

About the role

Key responsibilities & impact

Own the Apache Airflow platform, including environments, deployment, upgrades, configuration, connections, variables, pools, and DAG standards
Migrate orchestration from Azure Data Factory to Airflow and evolve the Metadata-Driven Orchestration Framework
Ensure Airflow reliability and observability through SLAs, alerting, retries, backfills, health monitoring, and failure ownership
Manage Azure resources with Terraform, including Databricks workspaces, Data Lake Storage, Data Factory, Key Vault, networking, and supporting services
Own secrets, identity, and access management using Key Vault, service principals, managed identities, Azure RBAC, Entra ID, and Unity Catalog
Own Azure DevOps repositories, CI/CD pipelines, branch policies, multi-environment promotion, deployment validation, and Terraform state
Track Azure resource cost and governance through tagging, budgets, right-sizing, and cost attribution
Contribute to the Data Catalog, Data Quality Framework, and platform-wide lineage
Drive observability for debuggable, alert-driven pipelines with clear failure ownership
Operate Databricks and Unity Catalog, including cluster and job policies, Auto Loader ingestion, and workload performance and cost optimization
Participate in architecture reviews and technical decision-making
Define and promote engineering standards through code reviews and design reviews
Collaborate with Architects, Product Owners, and data engineers on data product technical maturation
Build and maintain developer tooling and automation, including AI-assisted engineering workflows
Provide second-line support to the Run/Operations team
Troubleshoot platform incidents, lead root cause analyses and post-mortems
Ensure handover, documentation, and operational readiness of platform changes
Improve runbooks, monitoring, and operational processes

Requirements

What you’ll need

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
Experience with access management: Azure RBAC, Entra ID, Unity Catalog permissions
Experience with Azure DevOps (repos, pipelines, service connections, environments) and CI/CD for data platforms
Experience with ADLS, networking and security basics, Azure Data Factory
Experience with Infrastructure as Code (Terraform) and multi-environment promotion
Hands-on experience with Databricks and Unity Catalog (compute, jobs, governance)
Experience with 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
Strong problem-solving and analytical thinking
Ability to work in cross-functional teams and influence without authority
Ownership mindset and attention to detail
Good communication skills with technical and business stakeholders
Role requires permanent contract or B2B arrangement