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Teya is a Series C fintech platform serving local businesses across Europe—cafés, restaurants, salons, shops, and entrepreneurs—with financial tools, thoughtful design, and human support. The company is building a modern financial platform to replace clunky legacy systems that have historically made life harder for small business owners.
You will join the Data Engineering team as a Senior Data Engineer focused on the data platform. This is a hands-on technical role with significant architectural influence. You will own the design and evolution of Teya's data infrastructure, working across the entire data lifecycle from ingestion pipelines and their underlying infrastructure through orchestration and governance. You'll help expand platform capabilities to power decision-making across the organization and enable future AI initiatives.
Key responsibilities include:
- Designing and evolving the architecture of data infrastructure
- Maintaining and improving the reliability of data ingestion pipelines
- Ensuring proper data governance while keeping processes simple and user-friendly
- Improving data reliability, quality, and observability across key datasets
- Building and maintaining data models for large, complex datasets with simple lifecycle management
- Designing and optimizing ETL/ELT pipelines to support scalable analytics and data products
- Collaborating with data analysts, analytics engineers, and engineering teams to turn use cases into production solutions
- Participating in on-call rotation, investigating production incidents, and driving improvements
- Contributing to technical discussions, code reviews, and maintaining strong engineering standards
- Creating and maintaining technical documentation and operational runbooks
You will think deeply about architectural trade-offs: centralized vs. decentralized data provisioning, balancing governance rigor with ease-of-use and data democratization, and designing simple processes for managing all phases of the data lifecycle.
REQUIREMENTS:
- 5+ years of experience in Data Engineering, Data Platform Engineering, or Software Engineering working with data
- Strong SQL expertise, particularly in complex analytical and warehouse queries
- Proficiency in Python and/or Java
- Experience with data warehouses such as Snowflake or Apache Iceberg
- Practical knowledge deploying and operating containerized workloads using Docker and Kubernetes
- Knowledge of streaming tooling (Kafka, Kafka Connect, Flink, or similar)
- Experience with ETL/ELT tooling (dbt, Airflow, Airbyte, dlt, or similar)
- Hands-on experience provisioning and managing cloud infrastructure with Terraform
- Experience with CI/CD pipelines, automated testing, and Git-based development workflows
- Familiarity with observability practices including logging, metrics, alerting, and production troubleshooting
- Strong grasp of software engineering principles and best practices
- Experience contributing to or leading data warehouse architecture or redesign initiatives
- Ability to collaborate effectively with technical and non-technical stakeholders
- Strong experience with data warehousing, dimensional modeling, and data architecture
