At PRODYNA, we design, implement, and operate modern cloud-native software and data platforms for mid- to large-sized enterprises. We help our customers unlock the value of their data through scalable architectures, advanced analytics, and cloud technologies while delivering future-proof solutions built on Microsoft Azure.

As part of our growing Data Engineering team in Athens, you will design and build enterprise-grade data platforms, modern Lakehouse architectures, and high-performance data pipelines that power analytics and AI workloads. Working closely with architects, software engineers, and cloud specialists, you'll help customers transform complex business requirements into reliable, scalable, and secure data solutions.

Your Tasks

Design, build and maintain scalable batch and streaming data pipelines using Python, Apache Spark and Azure Databricks.
Develop modern ETL/ELT architectures capable of processing large-scale enterprise datasets.
Design and implement Lakehouse solutions using Delta Lake and other modern table formats.
Build robust APIs and data services for internal and external consumers.
Optimize Spark applications through performance tuning, partitioning strategies and bottleneck analysis.
Implement dimensional data models, Slowly Changing Dimensions (SCD), schema evolution and data quality controls.
Develop real-time streaming solutions using Spark Structured Streaming.
Collaborate with Cloud Architects and Software Engineers to design end-to-end Azure-based data platforms.
Build Infrastructure as Code using Terraform/Terragrunt and support CI/CD automation.
Ensure observability through logging, monitoring and operational metrics.
Apply security best practices including encryption, secrets management and protection of sensitive data.
Participate in customer workshops, architecture discussions and technical consulting activities.

Professional Data Engineer

3+ years of experience in Data Engineering or Backend Engineering.
Strong Python development skills with good understanding of software engineering principles.
Experience with Apache Spark / PySpark.
Hands-on experience with Azure Databricks.
Experience designing scalable ETL/ELT pipelines.
Good understanding of relational and analytical data modeling.
Experience developing REST APIs.
Familiarity with Azure services (Storage Accounts, Key Vault, Event Hubs, Azure Functions, etc.).
Experience with Git and CI/CD practices.
Strong communication skills and customer-oriented mindset.
Fluency in English.

Senior Data Engineer

Additionally, Senior candidates should demonstrate:

6+ years of professional experience in Data Engineering.
Proven experience designing enterprise-scale data platforms.
Deep expertise in Spark performance optimization.
Strong experience with streaming architectures and Spark Structured Streaming.
Experience with Lakehouse technologies such as Delta Lake, Apache Iceberg or Apache Hudi.
Advanced knowledge of data modeling, schema evolution and enterprise data architecture.
Experience implementing Infrastructure as Code using Terraform/Terragrunt.
Knowledge of observability, monitoring and production operations.
Strong understanding of security, encryption, key management and GDPR/PII handling.
Ability to mentor engineers and provide technical leadership.
Experience working directly with enterprise customers and stakeholders.

Compensation & Perks

Salary: We will settle on the exact compensation amount based on prior experience and skills.
Private health insurance & Life Insurance from day #1
Health management scheme (weekly sessions &monthly challenges)
25 vacation days
Team events tech oriented and more
International network
Lunch in the office & Go For Eat vouchers
Employee referral programme/ bonus

Dedicated budget for:

Employee education ~ 800€
Hardware selection (MacBook or Lenovo ThinkPad) with your own mobile ~ 3000€

PRODYNA remote jobs

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Data Engineer

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