Senior Data Engineer – Data Quality

Navarino

. Design, develop, and optimize scalable data pipelines and ETL/ELT processes.

Posted 5/25/2026full-timePiraeus • 🇬🇷 GreeceSeniorWebsite

Tech Stack

Tools & technologies

AirflowApacheAWSAzureCloudETLGoogle Cloud PlatformJenkinsKafkaPythonScalaSparkSQL

About the role

Key responsibilities & impact

Design, develop, and optimize scalable data pipelines and ETL/ELT processes.
Define and implement enterprise-wide data quality principles, frameworks, and standards.
Ensure data pipelines deliver reliable, accurate, and high-quality data across platforms and business domains.
Design and implement strategies that make data Findable, Accessible, Interoperable, and Reusable (FAIR).
Build and maintain scalable datasets and data models that support analytics and AI/ML initiatives.
Collaborate closely with AI, Data Science, Analytics, and Engineering teams to support AI-related projects and production workloads.
Ensure data assets are cataloged, and metadata (business and technical) is properly maintained to improve discoverability and trust.
Work with engineers, analysts, and business stakeholders to define data quality requirements for dashboards, models, and operational processes.
Drive best practices across data architecture, governance, testing, monitoring, documentation, and CI/CD processes.
Support cloud-native and multi-cloud data solutions across different cloud providers.
Improve observability, reliability, security, and operational excellence across the data platform.

Requirements

What you’ll need

Bachelor’s degree in Computer Science, Data Management, Information Systems, or a related field.
Strong hands-on experience in Data Engineering or Data Quality roles.
Proven experience designing and managing modern data pipelines and large-scale datasets.
Strong SQL skills and proficiency in programming languages such as Python, Spark, or Scala.
Experience with pipeline orchestration and modern data tooling.
Exposure to cloud platforms such as AWS, Azure, and/or Google Cloud Platform.
Excellent communication and stakeholder management skills.
Strong focus on operational excellence, automation, scalability, and continuous improvement.
*Nice to have:*
Experience collaborating with AI/ML or Data Science teams and supporting AI-driven initiatives.
Track record implementing and managing data quality frameworks (e.g., **Great Expectations, Soda, or Deequ**) within modern data platforms.
Experience with **DataOps and/or MLOps** practices, including CI/CD for data using tools like **GitHub Actions, GitLab CI, or Jenkins.**
Exposure to streaming or real-time data architectures using **Apache Kafka, Confluent, or Amazon Kinesis.**
Experience working in multi-cloud or hybrid-cloud environments (AWS, Azure, GCP).
Experience with modern orchestration engines like **Apache Airflow or Dagster **
Familiarity with **Vector Databases **(e.g., **Pinecone, Qdrant, or Weaviate**) to support Retrieval-Augmented Generation (RAG) and AI initiatives.
Strong understanding of data governance, metadata management, and data lifecycle best practices.

Benefits

Comp & perks

An attractive financial package
A generous yearly bonus based on overall company performance and your contributions to the team’s success
Excellent working conditions with a strong work-life balance
A wide variety of benefits, including private health insurance
Personal development and training opportunities to support your professional growth and continuous learning
A working environment certified as a "Great Place to Work" for four consecutive years (2022–2025), a "Best Place to Work- Tech" for 2025 and "Best Place to Work- Hellas" 2026

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Hard Skills & Tools

data engineeringdata qualitydata pipelinesSQLPythonSparkScalaDataOpsMLOpsdata governance

Soft Skills

communicationstakeholder managementoperational excellenceautomationscalabilitycontinuous improvement