Data Engineer

The successful candidate will be responsible for designing and deploying robust data ingestion pipelines, managing Data Warehouse administration, and creating processes to transform raw information into clean, organized, and secure data for analysts. As core systems stabilize, the role will expand to explore and implement AI capabilities, taking ownership of the data lifecycle from raw source to production-ready analytics and automated AI solutions. The position involves working with leading tools and technologies such as Google Cloud Platform, Datastream, Microsoft Fabric, Databricks, and dbt, and offers a unique opportunity to define and lead the company's initial expansion into AI, developing internal AI tools, agents, and intelligent workflows.

1. Design, create, and deploy robust data ingestion pipelines to continuously feed the Data Warehouse with raw data from diverse sources using cloud-hosted ingestion tools.
2. Create and maintain efficient processes to transform raw data into clean, organized, and analyst-ready datasets using dbt, SQL, and Google Cloud tools (Dataflow, Datastream).
3. Act as the guardian of Analytics data, taking full responsibility for data quality, consistency, and the implementation of strict security protocols.
4. Define and implement data governance rules to ensure data integrity and compliance across the organization.
5. Manage the administration of the Data Warehouse, ensuring optimal performance, organization, and accessibility.
6. Implement, develop, and maintain the necessary Analytics Engineering tools and infrastructure to support the wider data team.
7. Actively assist in defining and evolving the data architecture to ensure it remains scalable and efficient as the business grows.
8. Work on the company’s initial AI initiatives by developing custom agents, tools, and intelligent workflows that leverage our data foundation to automate complex business processes.

1. At least 3 years of proven experience in a Data Engineering or Back-end Engineering role.
2. Advanced proficiency in SQL and strong coding skills in Python.
3. Deep understanding of modern Data Warehouse technologies, architectural patterns, and industry best practices.
4. Expertise with the latest tools and processes for data ingestion, transformation, and management (ETL/ELT).
5. Hands-on experience with Google Cloud solutions, specifically Cloud Storage, BigQuery, Datastream, and Dataflow (a plus).
6. Practical experience with dbt (data build tool) (a plus).
7. Familiarity with modern AI patterns, specifically Vector Databases for retrieval, managing context (RAG), and equipping LLMs with tools to perform actions and interact with external APIs (a plus).
8. Experience working with non-relational databases or Big Data technologies (a plus).
9. Familiarity with data streaming analytics and real-time data processing (a plus).
10. Proficiency with Git (a plus).
11. Knowledge of other coding languages beyond Python and SQL (a plus).
12. Rigorous attention to detail, with a high standard for data quality and accuracy.
13. Ability to work independently and proactively, anticipating problems before they happen.
14. Self-starter and target-oriented, capable of managing own roadmap to meet delivery goals.
15. Dedicated team player and good communicator, able to bridge the gap between technical complexity and business needs.