Senior/Lead Data Engineer - Data Quality

About us Navarino is an innovative global technology company with offices in Greece, Norway, Germany, Cyprus, the United Kingdom, Hong Kong, USA, UAE, Japan and Singapore. We develop technology solutions for the shipping industry and are a leader in our sector. Our R&D and engineering departments focus on building and enriching our product portfolio, with specialized software and services that we develop in-house. We pride ourselves on our people and culture. We encourage innovative thinking, teamwork, and excellence. Our committed people, our values and ways of working create a dynamic, professional, fun, and family-oriented environment which delivers high value and excellence to our customers. What will you be doing? We are looking for a hands-on Senior/Lead Data Engineer with strong expertise in Data Quality to help shape and scale our modern data ecosystem and enable data-driven, AI-centric applications across the organization.  As a member of the broader AI team at Navarino, this role will work closely with engineering and business stakeholders to design and build trusted, scalable, and well-governed data platforms that support analytics, operational reporting, machine learning, and next-generation AI solutions.  The ideal candidate brings strong data engineering expertise, a passion for data quality and governance, and a commitment to driving best practices across the entire data lifecycle. Responsibilities • 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.