Senior Data Engineer (Data Platform)

We are seeking a Senior Data Engineer to join our team and help shape the future of our data platform. This role focuses on data platform, building and maintaining the core data infrastructure, working on the entire lifecycle of data from ingestion pipelines and their infrastructure to orchestration and governance. You will play a key role in expanding the capabilities of our data platform, helping build new features that power decision-making across the organisation. This includes every stage of an ELT process, both at the pipeline level and the underlying infrastructure, as well as working with engineering teams on how to best leverage our data. As a senior member of the Data Engineering team, you will contribute to platform architecture, data product thinking, and engineering best practices, helping build a world-class scalable Data Platform that enables both analytics and future AI capabilities.

Responsibilities

Designing and evolving the architecture of our data infrastructure; Maintaining the existing data platform; Ensuring the reliability of our data ingestion pipelines; Propagating learnings in the Data Engineering team to other parts of Teya; Guaranteeing proper data governance following best practices, while not adding 100 steps of bureaucracy; Improving data reliability, quality, and observability across key datasets, while ensuring data pipelines are simple to set up; Building, maintaining, and improving data models for large and complex datasets, while ensuring they have a simple lifecycle; Designing and optimising ETL/ELT pipelines to support scalable analytics and data products; Collaborating with data analysts, analytics engineering and engineering teams to turn their data platform use cases into reliable production solutions; Participating in the team's on-call rotation, investigating production incidents and contributing to improvements across Teya to prevent them from recurring; Participating in technical discussions and code reviews, contributing to maintainable designs and strong engineering standards; Creating and maintaining clear technical documentation and operational runbooks

Requirements

5+ years experience in Data Engineering or 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 containerised workloads using Docker and Kubernetes; Knowledge of streaming tooling (Kafka, Kafka Connect, Flink,…); Experience with ETL/ELT tooling (dbt, Airflow, Airbyte, dlt,..); 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 modelling, and data architecture