Data Engineer

Stoiximan

. Build and maintain the lakehouse following medallion standards, including layer design, naming conventions, environments, and data quality rules .

Posted 11/7/2026full-timeAthens • GreeceMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and maintaining lakehouse architectures on Azure, implementing data governance and security measures, and developing robust data ingestion pipelines. Proficient in data modeling and CI/CD practices, with a strong focus on data quality and observability.

Highest-signal resume keywords

Data Engineering ExperienceAzure Lakehouse ExperienceStrong Python ProgrammingCI/CD ImplementationData Modeling Expertise

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonPySparkSQLData ModelingCI/CDBatch IngestionStreaming IngestionData Quality PracticesOrchestration ToolsInfrastructure as Code

Soft Skills

Clear CommunicationCollaboration with Non-Technical Colleagues

Tools & Technologies

DatabricksAzure SynapseDelta LakeGitAirflowData FactoryTerraformBicepKafkaEvent Hubs

Industry Keywords

Lakehouse ArchitectureData GovernanceData SecurityMetadata ManagementData Quality Checks

Tech Stack

Tools & technologies

AirflowAzureCloudKafkaPySparkPythonSQLTerraformUnity

About the role

Key responsibilities & impact

Build and maintain the lakehouse following medallion standards, including layer design, naming conventions, environments, and data quality rules
Deliver large, multi-source data projects from design through production
Develop reliable, idempotent batch and streaming ingestion pipelines from databases, including CDC, APIs, and files
Build and maintain silver and gold pipelines for trusted, performant BI data
Implement security and governance, including access control, masking, data classification, lineage, and metadata
Build observability into pipelines through monitoring, alerting, data quality checks, and incident procedures
Deploy through CI/CD and support infrastructure as code
Help monitor platform reliability and cloud costs
Contribute to technical decisions and code reviews
Maintain documentation and runbooks in repositories

Requirements

What you’ll need

3+ years in data engineering, with large, multi-source projects delivered in production
Production lakehouse experience on Azure (Databricks, Fabric, or Synapse) with Delta Lake, including a catalog and governance tool such as Unity Catalog
Strong Python, PySpark, and SQL
Experience with Git, testing, and code review
Solid data modeling, including star schemas and slowly changing dimensions
Experience with an orchestration tool such as Airflow, Data Factory, Dagster, or Databricks Workflows
Experience with CI/CD
Familiarity with infrastructure as code, such as Terraform or Bicep
Understanding of data security, including access management, encryption, and masking
Understanding of data quality practices
Experience with streaming technologies such as Kafka, Event Hubs, or Structured Streaming is a plus
Clear English and comfort working with non-technical colleagues, including agreeing metrics and business rules with BI analysts
dbt or similar is a plus

Benefits

Comp & perks

Hybrid flexibility - work your way
Competitive pay & annual bonus - rewarding great performance
Comprehensive health & life insurance - for you and your loved ones
Private pension plan - investing in your future
Nursery/childcare voucher - supporting working parents
Monthly lunch allowance - enjoy good food on us
Onboarding buddy - because starting strong matters
Access to Udemy & ongoing learning - keep growing your skills
360° peer feedback - real insights, real growth
Wellbeing perks - gym discounts, nutrition sessions & more
Fresh fruit, always - stay energized throughout the day