Senior Data Engineer
At a glance
Senior Data Engineering role at Kpler. Athens · full-time.
Pay not stated Data Engineer salary
dataroles summary, based on the employer's posting.
What you'll do
- Design, deploy and run production data pipelines, carrying them from initial architecture through monitoring and ongoing upkeep
- Build Python services for AWS and package them with Docker, Kubernetes, Airflow and Terraform-based infrastructure
- Structure PostgreSQL storage and Kafka or Flink streams to keep data moving accurately through the system
- Set pipeline design standards, review peers’ work and establish logging, monitoring and alerting practices
- Work with engineers, product managers and data scientists, mentor fellow engineers, and assess new infrastructure tools
What you bring
- At least five years designing and operating production data pipelines and their supporting infrastructure
- Strong Python skills and practical use of Docker, Kubernetes, AWS, Airflow and relational databases such as PostgreSQL
- Experience with Kafka or Flink, Terraform, CI/CD, and production logging and monitoring systems
- A record of guiding architecture discussions and mentoring engineers, plus sound judgment when using AI coding tools
- Agile experience and clear written and spoken English
Who this fits
This senior role suits a hands-on data engineer ready to lead architecture and pipeline work while mentoring peers. The position is hybrid in Athens, Greece.
From the employer
About the Role
As a Senior Data Engineer joining our new Marine Insurance initiative, you will play a central role in shaping the systems that power Kpler’s global market intelligence platform. You will lead the end-to-end design and delivery of complex data pipelines and cloud infrastructure, ingesting, processing, and serving large-scale data to our global customers. Acting as a technical leader, you will set data architecture directions and mentor peer engineers, ensuring our platform reliably scales as our datasets and product offerings expand.
Key Responsibilities
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Design and own end-to-end data pipelines: Build, deploy, and operate production-grade data pipelines and infrastructure from initial architecture through deployment, monitoring, and maintenance.
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Deploy containerized AWS services: Develop and containerize applications within AWS using Python, Docker, Kubernetes, Airflow, and Infrastructure as Code (Terraform).
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Ensure data integrity and event streaming: Architect robust PostgreSQL database structures and event-streaming flows (Kafka/Flink) to guarantee seamless data flow and integrity.
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Set technical direction and standards: Lead complex pipeline design, peer reviews, and logging, monitoring, and alerting frameworks to maintain pipeline resilience.
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Partner cross-functionally and mentor peers: Collaborate closely with engineers, product managers, and data scientists while mentoring engineers to elevate team craft and technical development.
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Evaluate emerging data tools: Research and implement modern data infrastructure improvements and frameworks to continuously elevate Kpler's platform capabilities.
Experience & Background
- What you'll need (Must-haves)
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Data engineering experience at scale: 5+ years of experience designing, operating, and owning production-grade data pipelines and infrastructure.
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Core tech stack depth: Strong Python skills with hands-on experience in Docker, Kubernetes, AWS, Airflow, and relational databases like PostgreSQL.
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Streaming, IaC, and observability: Practical experience with event streaming (Kafka/Flink), Infrastructure as Code (Terraform), CI/CD practices, and production logging/monitoring systems.
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Technical leadership & AI coding tools: Demonstrated ability to lead architectural discussions, mentor engineers, and leverage AI-assisted coding tools (Copilot, Cursor, Claude) with strong technical judgment.
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Methodologies & communication: Experience working in Agile environments with strong collaboration skills and fluency in written and spoken English.
Nice-to-haves
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Large-scale analytics platforms: Experience building large-scale data processing systems or analytics platforms.
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Cross-team initiatives: Track record of leading technical initiatives across multiple teams.
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Domain knowledge: Familiarity with commodities, shipping, energy, or marine insurance market data.
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